Ssp Class Php Download Pdf [TOP]

No - don't use the SSP class at all if you are using Editor. Editor's own libraries have built in support for server-side processing and will automatically detect a server-side processing request - example.

I guess my question still remains the same though. I'm assuming that the sample script is enough to generate a similar ajax accompanied by it in the examples page. So If I'm using DataTables without the Editor, how can I make the code work with ssp.class.php? Is it something I need to include in the Download Builder? Or do I need to add something else in the script?

As you've found out that won't take care of the require( 'ssp.class.php' ); statement. You need to download the full DataTables package found here. ssp.class.php can be found in examples/server_side/scripts.

If you extract the archive you've downloaded to a server of some sort you can browse to /examples to view a local version of DataTable's examples. Though for the server-side stuff you'll need to have some SQL server set up(the scripts directory has .sql files to populate the database for you).

The button 'Export CSV' is visible and the export is working, but only on the visible data on one page. The data are filtered according to selected filter, which is great. So I think the only problem is with pagination. I found several solutions. Many of them manages it by selecting "All" entries and then export, which is no the right way, because I will need to export thousands of rows. Definitely, I need to use a server-side process to create the files to download.

To export CSV, do I need special script where will be the data send or I can use the customised server_processing.php which is downloadable from datatables.net website? Is it the same with the export to EXCEL and PDF ?

Hi Kevin, thank you, I mentioned it in the question, that I need server-side tools to export data. I just figured it out with modifying the ssp.class.php script where I changed the $limit to export more rows than visible.

@culter, great to hear that you managed to find a way to get the server-side export working for csv files. Are you able to share your script of ssp.class.php to understand how achieved that and so that others can adapt in their project. Thanks

In a previous article, we have seen how to export tabular data to a CSV file using PHP. Also, we have seen how to do excel export from HTML table data. If you are searching for such custom PHP codes without using any external libraries like DataTables you can download the source from the linked tutorials.

The SSP class handles the database related operations. It contains some helper functions to build SQL queries for DataTables server-side processing with search and filter. You can see the code of the SSP library from here.

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One thing you are missing in your code snippet is orderFixed: [0, 'desc']. This is key to making this technique work. This needs to be a separate column from the checkbox column otherwise the 1 or 0 will display with the checkbox. Another issue is your are using className: 'select-checkbox', which displays a checkbox but I don't think its a normal HTML checkbox input so clicking it doesn't check it.

Another problem is you should use the Select Extension API's (row().select() and row().deselect()) to toggle the select state instead of toggling the selected class. By toggling the class the deselect event is never called.

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NAVSEA celebrates Black History Month, celebrating the contributions of African Americans to our nation. Sailors aboard the Arleigh-Burke-class guided-missile destroyer USS Winston S. Churchill (DDG 81) man the rails as the ship pulls in to port in Portsmouth, England.(U.S. Navy photo by Mass Communication Specialist 3rd Class Bounome Chanphouang/Released)

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The goal in the object tracking task is to estimate object tracklets for the classes 'Car' and 'Pedestrian'. We evaluate 2D 0-based bounding boxes in each image. We like to encourage people to add a confidence measure for every particular frame for this track. For evaluation we only consider detections/objects larger than 25 pixel (height) in the image and do not count Vans as false positives for cars or Sitting Persons as false positives for Pedestrians due to their similarity in appearance. As evaluation criterion we follow the HOTA metrics [1], while also evaluating the CLEARMOT [2] and Mostly-Tracked/Partly-Tracked/Mostly-Lost [3] metrics. Methods are ranked overall by HOTA, and bold numbers indicate the best method for each particular metric. To make the methods comparable, the time for object detection is not included in the specified runtime.[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.Note: On 25.02.2021 we have updated the evaluation to use the HOTA metrics as the main evaluation metrics, and to show results as plots to enable better comparison over various aspects of tracking. Furthermore, the definition of previously used evaluation metrics such as MOTA have been updated to match modern definitions (such as used in MOTChallenge) in order to unify metrics across benchmarks. Now ID-switches are counted for cases where the ID changes after a gap in either gt or predicted tracks, and when assigning IDs the algorithm has a preferences for extending current tracks (minimizing the number of ID-switches) if possible. We have re-calculated the results for all methods. Please download the new evaluation code. Please report these new numbers for all future submissions. The previous leaderboards before the changes will remain live for now and can be found here, but after some time they will stop being updated.Please address any questions or feedback about KITTI tracking or KITTI mots evaluation to Jonathon Luiten at luiten@vision.rwth-aachen.de.Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.Additional information used by the methods Stereo: Method uses left and right (stereo) images Laser Points: Method uses point clouds from Velodyne laser scanner GPS: Method uses GPS information Online: Online method (frame-by-frame processing, no latency) Additional training data: Use of additional data sources for training (see details)CARThis figure as: png pdf This figure as: png pdf This figure as: png pdf This figure as: png pdf Method Setting Code HOTA DetA AssA DetRe DetPr AssRe AssPr LocA MOTA 1 HRI-SFMOT 83.04 % 79.87 % 87.15 % 85.17 % 85.65 % 89.91 % 91.74 % 87.99 % 92.62 % 2 IMOU_ALG 82.08 % 78.78 % 86.21 % 84.83 % 83.98 % 90.00 % 89.84 % 87.14 % 92.75 % 3 VirConvTrack 81.87 % 78.14 % 86.39 % 82.00 % 86.92 % 89.08 % 91.58 % 88.04 % 90.24 % 4 CasTrack code 81.00 % 78.58 % 84.22 % 84.10 % 84.86 % 87.55 % 90.47 % 87.49 % 91.91 % H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE TGRS 2022.H. Wu, W. Han, C. Wen, X. Li and C. Wang: 3D Multi-Object Tracking in Point CloudsBased on Prediction Confidence-Guided DataAssociation. IEEE TITS 2021. 5 PC-TCNN 80.90 % 78.46 % 84.13 % 84.22 % 84.58 % 87.46 % 90.47 % 87.48 % 91.70 % H. Wu, Q. Li, C. Wen, X. Li, X. Fan and C. Wang: Tracklet Proposal Network for Multi-Object Tracking on Point Clouds. IJCAI 2021. 6 Rethink MOT 80.39 % 77.88 % 83.64 % 84.23 % 83.57 % 87.63 % 88.90 % 87.07 % 91.53 % L. Wang, J. Zhang, P. Cai and X. Li: Towards Robust Reference System for Autonomous Driving: Rethinking 3D MOT. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA) 2023. 7 MCMOT-RAM-DeepSort 80.02 % 78.85 % 81.86 % 82.60 % 86.32 % 85.34 % 88.41 % 87.14 % 91.70 % 8 RAM 79.53 % 78.79 % 80.94 % 82.54 % 86.33 % 84.21 % 88.77 % 87.15 % 91.61 % P. Tokmakov, A. Jabri, J. Li and A. Gaidon: Object Permanence Emerges in a Random Walk along Memory. ICML 2022. 9 Anonymous 79.13 % 78.81 % 80.13 % 82.41 % 86.43 % 83.40 % 88.81 % 87.11 % 91.72 % 10 FastTrack 78.78 % 77.67 % 80.66 % 81.76 % 84.57 % 84.02 % 87.58 % 86.01 % 92.06 % 11 PA3DMOT 78.60 % 76.01 % 82.28 % 80.77 % 85.44 % 85.36 % 91.37 % 87.84 % 87.98 % 12 MCMOT Perma-DeepSort 78.54 % 78.43 % 79.29 % 81.86 % 86.53 % 82.31 % 89.16 % 87.10 % 91.55 % 13 MSA-MOT 78.52 % 75.19 % 82.56 % 82.42 % 82.21 % 85.21 % 90.16 % 87.00 % 88.01 % 14 CyberTrack 78.25 % 77.51 % 79.88 % 82.95 % 84.99 % 82.45 % 91.69 % 87.62 % 90.14 % 15 PermaTrack 78.03 % 78.29 % 78.41 % 81.71 % 86.54 % 81.14 % 89.49 % 87.10 % 91.33 % P. Tokmakov, J. Li, W. Burgard and A. Gaidon: Learning to Track with Object Permanence. ICCV 2021. 16 PC3T code 77.80 % 74.57 % 81.59 % 79.19 % 84.07 % 84.77 % 88.75 % 86.07 % 88.81 % H. Wu, W. Han, C. Wen, X. Li and C. Wang: 3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association. IEEE TITS 2021. 17 StrongSORT++ code 77.75 % 77.89 % 78.20 % 81.42 % 86.22 % 82.24 % 86.73 % 86.96 % 90.35 % 18 Loc Phenikaa-X 77.32 % 75.08 % 80.32 % 79.77 % 85.55 % 83.16 % 91.01 % 87.90 % 86.33 % 19 jerrymot 77.12 % 73.43 % 81.66 % 80.60 % 81.69 % 84.23 % 90.45 % 86.79 % 85.82 % 20 AT 76.59 % 76.27 % 77.47 % 79.59 % 86.30 % 80.95 % 87.70 % 86.97 % 89.03 % 21 OC-SORT code 76.54 % 77.25 % 76.39 % 80.64 % 86.36 % 80.33 % 87.17 % 87.01 % 90.28 % J. Cao, X. Weng, R. Khirodkar, J. Pang and K. Kitani: Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. 2022. 22 S3Track 76.52 % 77.50 % 76.26 % 81.29 % 85.88 % 79.42 % 88.61 % 86.95 % 90.31 % Anonymous: S$^3$Track: Self-supervised Tracking with Soft Assignment Flow. . 23 StrongFusion-MOT 75.65 % 72.08 % 79.84 % 75.20 % 86.23 % 82.42 % 89.81 % 86.74 % 85.53 % 24 Mono_3D_KF 75.47 % 74.10 % 77.63 % 78.86 % 82.98 % 80.23 % 88.88 % 85.48 % 88.48 % A. Reich and H. Wuensche: Monocular 3D Multi-Object Tracking with an EKF Approach for Long-Term Stable Tracks. 2021 IEEE 24th International Conference on Information Fusion (FUSION) 2021. 25 DeepFusion-MOT code 75.46 % 71.54 % 80.05 % 75.34 % 85.25 % 82.63 % 89.77 % 86.70 % 84.63 % X. Wang, C. Fu, Z. Li, Y. Lai and J. He: DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association. IEEE Robotics and Automation Letters 2022. 26 PolarMOT code 75.16 % 73.94 % 76.95 % 80.81 % 82.40 % 80.00 % 89.27 % 87.12 % 85.08 % A. Kim, G. Bras'o, A. O\vsep and L. Leal-Taix'e: PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?. European Conference on Computer Vision (ECCV) 2022. 27 EagerMOT code 74.39 % 75.27 % 74.16 % 78.77 % 86.42 % 76.24 % 91.05 % 87.17 % 87.82 % A. Kim, A. Osep and L. Leal-Taix'e: EagerMOT: 3D Multi-Object Tracking via Sensor Fusion. IEEE International Conference on Robotics and Automation (ICRA) 2021. 28 DEFT code 74.23 % 75.33 % 73.79 % 79.96 % 83.97 % 78.30 % 85.19 % 86.14 % 88.38 % M. Chaabane, P. Zhang, R. Beveridge and S. O'Hara: DEFT: Detection Embeddings for Tracking. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2021. 29 OC-SORT 74.20 % 76.75 % 72.36 % 80.15 % 86.30 % 76.30 % 87.10 % 87.00 % 89.31 % 30 CJMODT 74.09 % 75.86 % 72.99 % 78.59 % 87.36 % 75.20 % 90.90 % 87.44 % 87.44 % 31 TripletTrack 73.58 % 73.18 % 74.66 % 76.18 % 86.81 % 77.31 % 89.55 % 87.37 % 84.32 % N. Marinello, M. Proesmans and L. Van Gool: TripletTrack: 3D Object Tracking Using Triplet Embeddings and LSTM. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022. 32 MCMOT CenterTrack 73.39 % 74.85 % 72.58 % 80.19 % 83.11 % 75.56 % 87.93 % 86.15 % 87.92 % 33 FNC2 73.19 % 73.27 % 73.77 % 80.98 % 81.67 % 77.05 % 89.84 % 87.31 % 84.21 % H. Chao Jiang: A Fast and High-Performance Object Proposal Method for Vision Sensors: Application to Object Detection. IEEE sensors journal 2022. 34 mono3DT code 73.16 % 72.73 % 74.18 % 76.51 % 85.28 % 77.18 % 87.77 % 86.88 % 84.28 % H. Hu, Q. Cai, D. Wang, J. Lin, M. Sun, P. Krähenbühl, T. Darrell and F. Yu: Joint Monocular 3D Vehicle Detection and Tracking. ICCV 2019. 35 LGM 73.14 % 74.61 % 72.31 % 80.53 % 82.16 % 76.38 % 84.74 % 85.85 % 87.60 % G. Wang, R. Gu, Z. Liu, W. Hu, M. Song and J. Hwang: Track without Appearance: Learn Box and Tracklet Embedding with Local and Global Motion Patterns for Vehicle Tracking. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021. 36 Anonymous code 73.06 % 74.34 % 72.34 % 77.95 % 85.80 % 75.10 % 88.26 % 87.07 % 86.26 % 37 CenterTrack code 73.02 % 75.62 % 71.20 % 80.10 % 84.56 % 73.84 % 89.00 % 86.52 % 88.83 % X. Zhou, V. Koltun and P. Krähenbühl: Tracking Objects as Points. ECCV 2020. 38 QD-3DT code 72.77 % 74.09 % 72.19 % 78.13 % 85.48 % 74.87 % 89.21 % 87.16 % 85.94 % H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021. 39 TrackMPNN code 72.30 % 74.69 % 70.63 % 80.02 % 83.11 % 73.58 % 87.14 % 86.14 % 87.33 % A. Rangesh, P. Maheshwari, M. Gebre, S. Mhatre, V. Ramezani and M. Trivedi: TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking. arXiv preprint arXiv:2101.04206 . 40 DTFI 72.22 % 65.67 % 79.98 % 81.99 % 70.95 % 84.14 % 87.65 % 86.35 % 72.91 % 41 DiTMOT code 72.21 % 71.09 % 74.04 % 75.98 % 83.28 % 76.57 % 89.97 % 86.15 % 84.53 % S. Wang, P. Cai, L. Wang and M. Liu: DiTNet: End-to-End 3D Object Detection and Track ID Assignment in Spatio-Temporal World. IEEE Robotics and Automation Letters 2021. 42 GQY_tracking 72.05 % 69.46 % 75.15 % 78.20 % 79.09 % 81.42 % 85.07 % 86.54 % 80.98 % ERROR: Wrong syntax in BIBTEX file. 43 EAFFMOT 71.97 % 71.89 % 72.53 % 76.92 % 83.82 % 75.89 % 88.54 % 86.72 % 84.66 % 44 SMAT 71.88 % 72.13 % 72.13 % 74.43 % 87.33 % 74.77 % 88.30 % 87.19 % 83.64 % N. Gonzalez, A. Ospina and P. Calvez: SMAT: Smart Multiple Affinity Metrics for Multiple Object Tracking. Image Analysis and Recognition 2020. 45 NC2 71.85 % 69.61 % 74.81 % 81.19 % 76.99 % 78.57 % 89.33 % 87.30 % 78.52 % Chao Jiang and W. Zhiling: A New Adaptive Noise Covariance Matrices Estimation and Filtering Method: Application to Multi-Object Tracking. arXiv 2021. 46 CJMODT-v2 71.75 % 73.16 % 71.01 % 75.78 % 86.84 % 73.04 % 90.39 % 86.97 % 84.86 % 47 TuSimple 71.55 % 72.62 % 71.11 % 76.78 % 83.84 % 74.51 % 86.26 % 85.72 % 86.31 % W. Choi: Near-online multi-target tracking with aggregated local flow descriptor. Proceedings of the IEEE International Conference on Computer Vision 2015.K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016. 48 DetFlowTrack 71.52 % 72.87 % 70.89 % 79.56 % 82.98 % 73.47 % 90.64 % 87.79 % 83.34 % 49 CenterTube_RCNN 71.25 % 74.27 % 69.24 % 79.94 % 83.53 % 72.01 % 90.28 % 86.85 % 86.97 % 50 CJMODT-v3 71.09 % 73.19 % 69.63 % 75.81 % 86.95 % 71.73 % 90.06 % 87.13 % 84.75 % 51 BcMODT 71.00 % 73.62 % 69.14 % 78.86 % 83.97 % 72.34 % 88.70 % 86.93 % 85.48 % 52 JMODT code 70.73 % 73.45 % 68.76 % 78.67 % 84.02 % 72.46 % 88.02 % 86.95 % 85.35 % K. Huang and Q. Hao: Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving. 2021. 53 MOTC* 70.69 % 73.72 % 68.46 % 78.67 % 84.35 % 71.54 % 88.29 % 86.96 % 85.61 % 54 OC-SORT 70.22 % 71.97 % 69.44 % 77.20 % 81.00 % 74.31 % 84.17 % 84.40 % 87.01 % 55 AB3DMOT+PointRCNN code 69.99 % 71.13 % 69.33 % 75.66 % 84.40 % 72.31 % 89.02 % 86.85 % 83.61 % X. Weng, J. Wang, D. Held and K. Kitani: 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics. IROS 2020. 56 JRMOT code 69.61 % 73.05 % 66.89 % 76.95 % 85.07 % 69.18 % 88.95 % 86.72 % 85.10 % A. Shenoi, M. Patel, J. Gwak, P. Goebel, A. Sadeghian, H. Rezatofighi, R. Mart\'in-Mart\'in and S. Savarese: JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset. The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020. 57 MOTSFusion code 68.74 % 72.19 % 66.16 % 76.05 % 84.88 % 69.57 % 85.49 % 86.56 % 84.24 % J. Luiten, T. Fischer and B. Leibe: Track to Reconstruct and Reconstruct to Track. IEEE Robotics and Automation Letters 2020. 58 IMMDP 68.66 % 68.02 % 69.76 % 71.47 % 83.28 % 74.50 % 82.02 % 84.80 % 82.75 % Y. Xiang, A. Alahi and S. Savarese: Learning to Track: Online Multi- Object Tracking by Decision Making. International Conference on Computer Vision (ICCV) 2015.S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015. 59 SRK_ODESA(hc) 68.51 % 75.40 % 63.08 % 78.89 % 86.00 % 65.89 % 87.47 % 86.88 % 87.79 % D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking. ACCV 2020. 60 BAT 68.49 % 71.53 % 66.14 % 75.24 % 83.89 % 70.51 % 83.69 % 85.26 % 86.20 % 61 Quasi-Dense code 68.45 % 72.44 % 65.49 % 76.01 % 85.37 % 68.28 % 88.53 % 86.50 % 84.93 % J. Pang, L. Qiu, X. Li, H. Chen, Q. Li, T. Darrell and F. Yu: Quasi-Dense Similarity Learning for Multiple Object Tracking. CVPR 2021. 62 MASS 68.25 % 72.92 % 64.46 % 76.83 % 85.14 % 72.12 % 81.46 % 86.80 % 84.64 % H. Karunasekera, H. Wang and H. Zhang: Multiple Object Tracking with attention to Appearance, Structure, Motion and Size. IEEE Access 2019. 63 JCSTD 65.94 % 65.37 % 67.03 % 68.49 % 82.42 % 71.02 % 82.25 % 84.03 % 80.24 % W. Tian, M. Lauer and L. Chen: Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios. IEEE Transactions on Intelligent Transportation Systems 2019. 64 MDP code 64.79 % 63.04 % 67.05 % 66.18 % 82.22 % 69.61 % 85.61 % 84.24 % 76.08 % Y. Xiang, A. Alahi and S. Savarese: Learning to Track: Online Multi-Object Tracking by Decision Making. International Conference on Computer Vision (ICCV) 2015.Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017. 65 NOMT* 64.77 % 63.08 % 67.04 % 66.92 % 79.28 % 70.38 % 83.14 % 82.22 % 77.91 % W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. ICCV 2015. 66 SRK_ODESA(mc) 64.25 % 74.87 % 55.70 % 78.62 % 84.68 % 62.10 % 81.78 % 85.85 % 88.50 % D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking. ACCV 2020. 67 MOTBeyondPixels code 63.75 % 72.87 % 56.40 % 76.58 % 85.38 % 59.05 % 86.70 % 86.90 % 82.68 % S. Sharma, J. Ansari, J. Krishna Murthy and K. Madhava Krishna: Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018. 68 mmMOT code 62.05 % 72.29 % 54.02 % 76.17 % 84.89 % 58.98 % 82.40 % 86.58 % 83.23 % W. Zhang, H. Zhou, Sun, Z. Wang, J. Shi and C. Loy: Robust Multi-Modality Multi-Object Tracking. International Conference on Computer Vision (ICCV) 2019. 69 FANTrack code 60.85 % 64.36 % 58.69 % 69.17 % 80.82 % 60.78 % 88.94 % 84.72 % 75.84 % E. Baser, V. Balasubramanian, P. Bhattacharyya and K. Czarnecki: FANTrack: 3D Multi-Object Tracking with Feature Association Network. ArXiv 2019. 70 DSM 60.05 % 64.09 % 57.18 % 67.22 % 83.64 % 59.91 % 86.32 % 85.39 % 73.94 % D. Frossard and R. Urtasun: End-To-End Learning of Multi-Sensor 3D Tracking by Detection. ICRA 2018. 71 aUToTrack 59.83 % 67.82 % 53.68 % 72.66 % 79.60 % 55.94 % 86.52 % 83.10 % 80.97 % K. Burnett, S. Samavi, S. Waslander, T. Barfoot and A. Schoellig: aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge. arXiv:1905.08758 2019. 72 extraCK 59.76 % 65.18 % 55.47 % 69.21 % 81.69 % 61.82 % 75.70 % 84.30 % 79.29 % G. Gunduz and T. Acarman: A lightweight online multiple object vehicle tracking method. Intelligent Vehicles Symposium (IV), 2018 IEEE 2018. 73 3D-CNN/PMBM 59.12 % 65.43 % 54.28 % 69.87 % 80.68 % 57.28 % 83.89 % 83.94 % 79.23 % S. Scheidegger, J. Benjaminsson, E. Rosenberg, A. Krishnan and K. Granström: Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering. 2018 IEEE Intelligent Vehicles Symposium, IV 2018, Changshu, Suzhou, China, June 26-30, 2018 2018. 74 NOMT-HM* 59.08 % 61.27 % 57.45 % 65.14 % 79.29 % 60.25 % 83.46 % 82.63 % 74.66 % W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. ICCV 2015. 75 Point3DT 57.20 % 55.71 % 59.15 % 64.66 % 68.67 % 63.20 % 78.30 % 80.07 % 67.56 % Sukai Wang and M. Liu: PointTrackNet: An End-to-End Network for 3-D Object Detection and Tracking from Point Clouds. to be submitted ICRA'20 . 76 LP-SSVM* 56.62 % 61.02 % 52.80 % 65.32 % 76.83 % 55.61 % 80.07 % 80.92 % 76.82 % S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. International Journal of Computer Vision 2016. 77 MCMOT-CPD 56.61 % 64.28 % 50.55 % 67.37 % 82.77 % 53.96 % 81.97 % 84.26 % 77.98 % B. Lee, E. Erdenee, S. Jin, M. Nam, Y. Jung and P. Rhee: Multi-class Multi-object Tracking Using Changing Point Detection. ECCVWORK 2016. 78 NOMT 56.49 % 52.29 % 61.59 % 54.73 % 78.75 % 64.63 % 83.40 % 81.41 % 66.36 % W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. ICCV 2015. 79 SCEA* 56.09 % 60.70 % 52.15 % 64.97 % 77.83 % 54.87 % 81.17 % 81.94 % 74.92 % J. Yoon, C. Lee, M. Yang and K. Yoon: Online Multi-object Tracking via Structural Constraint Event Aggregation. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2016. 80 RMOT* 55.82 % 54.95 % 57.34 % 62.56 % 69.08 % 62.58 % 74.77 % 78.82 % 65.07 % J. Yoon, M. Yang, J. Lim and K. Yoon: Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. IEEE Winter Conference on Applications of Computer Vision (WACV) 2015. 81 CIWT* code 54.90 % 60.57 % 49.99 % 64.13 % 78.77 % 51.98 % 82.33 % 81.87 % 74.44 % A. Osep, W. Mehner, M. Mathias and B. Leibe: Combined Image- and World-Space Tracking in Traffic Scenes. ICRA 2017. 82 FAMNet 52.56 % 61.00 % 45.51 % 64.40 % 78.67 % 48.66 % 77.41 % 81.47 % 75.92 % P. Chu and H. Ling: FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking. ICCV 2019. 83 SASN-MCF_nano 52.24 % 59.65 % 46.22 % 66.28 % 77.27 % 56.20 % 68.77 % 84.56 % 69.82 % G. Gunduz and T. Acarman: Efficient Multi-Object Tracking by Strong Associations on Temporal Window. IEEE Transactions on Intelligent Vehicles 2019. 84 NOMT-HM 52.17 % 48.58 % 56.45 % 50.76 % 79.02 % 58.78 % 84.62 % 81.82 % 60.68 % W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. ICCV 2015. 85 SSP* code 51.16 % 58.96 % 44.64 % 65.26 % 74.09 % 46.75 % 80.78 % 81.32 % 70.94 % P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation. International Conference on Computer Vision (ICCV) 2015. 86 mbodSSP* code 50.92 % 58.57 % 44.51 % 63.69 % 75.67 % 46.47 % 81.23 % 81.44 % 70.78 % P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation. International Conference on Computer Vision (ICCV) 2015. 87 Complexer-YOLO 49.12 % 62.44 % 39.34 % 67.58 % 76.86 % 40.72 % 85.23 % 81.47 % 72.61 % M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019. 88 LP-SSVM 47.21 % 47.93 % 46.77 % 50.19 % 77.19 % 48.78 % 81.46 % 80.40 % 61.08 % S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. International Journal of Computer Vision 2016. 89 DCO-X* code 46.53 % 56.69 % 38.71 % 62.56 % 74.41 % 41.26 % 79.21 % 81.50 % 66.22 % A. Milan, K. Schindler and S. Roth: Detection- and Trajectory-Level Exclusion in Multiple Object Tracking. CVPR 2013. 90 Decoupled DeepSORT 45.75 % 48.46 % 43.77 % 51.97 % 73.58 % 50.52 % 65.79 % 79.06 % 59.09 % 91 RMOT 44.80 % 42.02 % 48.32 % 44.53 % 73.59 % 51.68 % 77.62 % 78.92 % 51.92 % J. Yoon, M. Yang, J. Lim and K. Yoon: Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. IEEE Winter Conference on Applications of Computer Vision (WACV) 2015. 92 CEM code 43.41 % 41.72 % 45.77 % 43.72 % 76.72 % 47.45 % 83.68 % 80.44 % 51.34 % A. Milan, S. Roth and K. Schindler: Continuous Energy Minimization for Multitarget Tracking. IEEE TPAMI 2014. 93 SCEA 43.06 % 44.75 % 41.70 % 46.22 % 80.08 % 43.11 % 84.22 % 81.84 % 56.00 % J. Yoon, C. Lee, M. Yang and K. Yoon: Online Multi-object Tracking via Structural Constraint Event Aggregation. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2016. 94 TBD code 43.01 % 43.06 % 43.30 % 44.50 % 79.49 % 44.94 % 84.22 % 81.47 % 53.94 % A. Geiger, M. Lauer, C. Wojek, C. Stiller and R. Urtasun: 3D Traffic Scene Understanding from Movable Platforms. Pattern Analysis and Machine Intelligence (PAMI) 2014.H. Zhang, A. Geiger and R. Urtasun: Understanding High-Level Semantics by Modeling Traffic Patterns. International Conference on Computer Vision (ICCV) 2013. 95 SORT 42.52 % 44.01 % 41.31 % 47.30 % 73.93 % 42.83 % 83.04 % 80.75 % 53.15 % A. Bewley, Z. Ge, L. Ott, F. Ramos and B. Upcroft: Simple online and realtime tracking. 2016 IEEE International Conference on Image Processing (ICIP) 2016. 96 SSP code 40.07 % 44.83 % 36.13 % 46.55 % 78.34 % 39.99 % 75.30 % 80.91 % 56.33 % P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation. International Conference on Computer Vision (ICCV) 2015. 97 mbodSSP code 39.49 % 43.94 % 35.82 % 45.72 % 77.85 % 36.95 % 84.35 % 80.76 % 54.10 % P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation. International Conference on Computer Vision (ICCV) 2015. 98 ODAMOT 37.05 % 46.53 % 30.07 % 49.91 % 73.20 % 32.46 % 78.19 % 79.26 % 57.03 % A. Gaidon and E. Vig: Online Domain Adaptation for Multi-Object Tracking. British Machine Vision Conference (BMVC) 2015. 99 FMMOVT 34.35 % 33.80 % 35.39 % 39.20 % 62.79 % 39.66 % 75.42 % 80.40 % 31.23 % F. Alencar, C. Massera, D. Ridel and D. Wolf: Fast Metric Multi-Object Vehicle Tracking for Dynamical Environment Comprehension. Latin American Robotics Symposium (LARS), 2015 2015. 100 MCF 33.98 % 35.97 % 32.32 % 36.87 % 79.67 % 33.65 % 82.48 % 81.31 % 44.40 % L. Zhang, Y. Li and R. Nevatia: Global data association for multi-object tracking using network flows.. CVPR . 101 HM 33.79 % 34.30 % 33.45 % 35.16 % 79.56 % 34.55 % 83.08 % 81.33 % 42.36 % A. Geiger: Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms. 2013. 102 DCO code 33.45 % 36.33 % 31.30 % 40.93 % 64.11 % 34.23 % 73.46 % 77.25 % 36.72 % A. Andriyenko, K. Schindler and S. Roth: Discrete-Continuous Optimization for Multi-Target Tracking. CVPR 2012. 103 DP-MCF code 25.97 % 35.69 % 19.12 % 36.76 % 78.84 % 28.98 % 39.84 % 81.19 % 36.89 % H. Pirsiavash, D. Ramanan and C. Fowlkes: Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. IEEE conference on Computer Vision and Pattern Recognition (CVPR) 2011. Table as LaTeX | Only published MethodsPEDESTRIANThis figure as: png pdf This figure as: png pdf This figure as: png pdf This figure as: png pdf Method Setting Code HOTA DetA AssA DetRe DetPr AssRe AssPr LocA MOTA 1 IMOU_ALG 57.15 % 55.38 % 59.67 % 60.38 % 70.63 % 65.71 % 71.80 % 77.42 % 72.20 % 2 FastTrack 55.10 % 52.72 % 57.88 % 58.39 % 69.99 % 63.01 % 71.77 % 78.22 % 67.92 % 3 OC-SORT code 54.69 % 50.82 % 59.08 % 55.68 % 70.94 % 64.09 % 73.36 % 78.52 % 65.14 % J. Cao, X. Weng, R. Khirodkar, J. Pang and K. Kitani: Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. 2022. 4 StrongSORT++ code 54.48 % 52.01 % 57.31 % 57.53 % 70.06 % 63.85 % 70.09 % 78.30 % 67.38 % 5 OC-SORT 53.22 % 48.72 % 58.39 % 53.56 % 70.83 % 63.67 % 72.82 % 78.91 % 61.12 % 6 Anonymous 52.72 % 53.55 % 52.21 % 58.87 % 70.15 % 59.50 % 65.00 % 77.69 % 68.37 % 7 RAM 52.71 % 53.55 % 52.19 % 58.86 % 70.17 % 59.49 % 64.99 % 77.70 % 68.40 % P. Tokmakov, A. Jabri, J. Li and A. Gaidon: Object Permanence Emerges in a Random Walk along Memory. ICML 2022. 8 OC-SORT 52.12 % 47.93 % 56.87 % 52.18 % 72.12 % 61.72 % 73.14 % 79.34 % 60.16 % 9 SRK_ODESA(hp) 50.87 % 53.43 % 48.78 % 57.79 % 72.90 % 53.45 % 71.33 % 78.81 % 68.04 % D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking. ACCV 2020. 10 AT 50.73 % 50.78 % 50.94 % 54.80 % 72.28 % 58.02 % 65.85 % 78.39 % 65.65 % 11 PermaTrack 48.63 % 52.28 % 45.61 % 57.40 % 71.03 % 49.63 % 73.28 % 78.57 % 65.98 % P. Tokmakov, J. Li, W. Burgard and A. Gaidon: Learning to Track with Object Permanence. ICCV 2021. 12 HRI-SFMOT 46.82 % 41.26 % 53.40 % 45.69 % 58.80 % 57.45 % 65.12 % 71.29 % 49.16 % 13 FNC2 46.55 % 46.82 % 46.68 % 53.01 % 59.38 % 50.84 % 65.82 % 72.07 % 56.05 % H. Chao Jiang: A Fast and High-Performance Object Proposal Method for Vision Sensors: Application to Object Detection. IEEE sensors journal 2022. 14 3D-TLSR 46.34 % 42.03 % 51.32 % 44.51 % 71.14 % 54.45 % 73.11 % 76.87 % 53.58 % U. Nguyen and C. Heipke: 3D Pedestrian tracking using local structure constraints. ISPRS Journal of Photogrammetry and Remote Sensing 2020. 15 TuSimple 45.88 % 44.66 % 47.62 % 47.92 % 69.51 % 52.04 % 69.88 % 76.43 % 57.61 % W. Choi: Near-online multi-target tracking with aggregated local flow descriptor. Proceedings of the IEEE International Conference on Computer Vision 2015.K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016. 16 CAT 45.65 % 42.43 % 49.55 % 45.89 % 67.79 % 53.20 % 71.97 % 75.90 % 51.96 % U. Nguyen, F. Rottensteiner and C. Heipke: CONFIDENCE-AWARE PEDESTRIAN TRACKING USING A STEREO CAMERA. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 2019. 17 MPNTrack code 45.26 % 43.74 % 47.28 % 53.62 % 58.30 % 52.18 % 68.47 % 75.93 % 46.23 % G. Brasó and L. Leal-Taixé: Learning a Neural Solver for Multiple Object Tracking. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.G. Bras\'o, O. Cetintas and L. Leal-Taix\'e: Multi-Object Tracking and Segmentation Via Neural Message Passing. International Journal of Computer Vision 2022. 18 MSA-MOT 44.73 % 40.93 % 49.34 % 47.09 % 55.94 % 52.88 % 65.83 % 71.21 % 47.86 % 19 NC2 44.30 % 42.31 % 46.75 % 52.97 % 52.43 % 50.91 % 65.83 % 72.08 % 44.18 % Chao Jiang and W. Zhiling: A New Adaptive Noise Covariance Matrices Estimation and Filtering Method: Application to Multi-Object Tracking. arXiv 2021. 20 jerrymot 44.21 % 39.39 % 50.12 % 44.81 % 56.35 % 54.63 % 64.47 % 71.15 % 46.34 % 21 SRK_ODESA(mp) 43.73 % 53.73 % 36.05 % 58.01 % 73.19 % 40.05 % 69.44 % 78.91 % 67.31 % D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking. ACCV 2020. 22 PolarMOT code 43.59 % 39.88 % 48.12 % 44.90 % 57.40 % 51.95 % 65.22 % 71.34 % 46.98 % A. Kim, G. Bras'o, A. O\vsep and L. Leal-Taix'e: PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?. European Conference on Computer Vision (ECCV) 2022. 23 StrongFusion-MOT 43.42 % 38.86 % 48.83 % 45.30 % 53.81 % 52.54 % 63.17 % 70.53 % 39.04 % 24 Be-Track 43.36 % 39.99 % 47.23 % 43.00 % 69.03 % 51.28 % 69.60 % 76.78 % 50.85 % M. Dimitrievski, P. Veelaert and W. Philips: Behavioral Pedestrian Tracking Using a Camera and LiDAR Sensors on a Moving Vehicle. Sensors 2019. 25 BAT 42.91 % 43.91 % 42.22 % 47.19 % 71.11 % 45.25 % 73.40 % 77.92 % 55.71 % 26 Mono_3D_KF 42.87 % 40.13 % 46.31 % 46.02 % 59.91 % 52.86 % 63.50 % 74.03 % 45.44 % A. Reich and H. Wuensche: Monocular 3D Multi-Object Tracking with an EKF Approach for Long-Term Stable Tracks. 2021 IEEE 24th International Conference on Information Fusion (FUSION) 2021. 27 TripletTrack 42.77 % 39.54 % 46.54 % 41.97 % 71.91 % 50.86 % 71.26 % 77.93 % 50.08 % N. Marinello, M. Proesmans and L. Van Gool: TripletTrack: 3D Object Tracking Using Triplet Embeddings and LSTM. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022. 28 MDP code 42.76 % 39.23 % 47.13 % 43.83 % 63.02 % 50.91 % 71.04 % 75.15 % 47.02 % Y. Xiang, A. Alahi and S. Savarese: Learning to Track: Online Multi-Object Tracking by Decision Making. International Conference on Computer Vision (ICCV) 2015.Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017. 29 CJMODT 42.38 % 46.82 % 38.73 % 51.16 % 71.41 % 42.30 % 72.91 % 79.26 % 56.55 % 30 CJMODT-v3 41.44 % 43.54 % 39.88 % 47.98 % 68.95 % 43.47 % 73.29 % 78.27 % 51.43 % 31 CJMODT-v2 41.42 % 44.41 % 39.03 % 47.84 % 71.82 % 42.77 % 72.94 % 78.69 % 54.09 % 32 Quasi-Dense code 41.12 % 44.81 % 38.10 % 48.55 % 70.39 % 41.02 % 72.47 % 77.87 % 55.55 % J. Pang, L. Qiu, X. Li, H. Chen, Q. Li, T. Darrell and F. Yu: Quasi-Dense Similarity Learning for Multiple Object Tracking. CVPR 2021. 33 QD-3DT code 41.08 % 44.01 % 38.82 % 48.96 % 67.19 % 42.09 % 72.44 % 77.38 % 51.77 % H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021. 34 NOMT* 40.91 % 37.52 % 44.79 % 40.94 % 65.86 % 50.53 % 65.94 % 75.94 % 47.08 % W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. ICCV 2015. 35 CenterTrack code 40.35 % 44.48 % 36.93 % 49.91 % 66.83 % 41.05 % 70.19 % 77.81 % 53.84 % X. Zhou, V. Koltun and P. Krähenbühl: Tracking Objects as Points. ECCV 2020. 36 HMM 39.97 % 44.34 % 36.41 % 51.33 % 62.62 % 48.06 % 49.13 % 76.11 % 52.61 % 37 EAFFMOT 39.81 % 35.47 % 44.90 % 38.45 % 59.46 % 48.53 % 63.17 % 71.24 % 41.45 % 38 DetFlowTrack 39.64 % 40.90 % 38.72 % 47.54 % 56.35 % 41.48 % 66.03 % 72.04 % 46.75 % 39 RMOT* 39.56 % 36.07 % 43.63 % 39.74 % 63.97 % 49.54 % 62.82 % 75.35 % 43.32 % J. Yoon, M. Yang, J. Lim and K. Yoon: Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. IEEE Winter Conference on Applications of Computer Vision (WACV) 2015. 40 JCSTD 39.44 % 34.20 % 45.79 % 36.15 % 69.39 % 49.38 % 69.00 % 76.23 % 43.42 % W. Tian, M. Lauer and L. Chen: Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios. IEEE Transactions on Intelligent Transportation Systems 2019. 41 TrackMPNN code 39.40 % 44.24 % 35.45 % 50.78 % 64.58 % 38.98 % 69.80 % 77.56 % 52.10 % A. Rangesh, P. Maheshwari, M. Gebre, S. Mhatre, V. Ramezani and M. Trivedi: TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking. arXiv preprint arXiv:2101.04206 . 42 EagerMOT code 39.38 % 40.60 % 38.72 % 43.43 % 61.49 % 40.98 % 68.33 % 71.25 % 49.82 % A. Kim, A. Osep and L. Leal-Taix'e: EagerMOT: 3D Multi-Object Tracking via Sensor Fusion. IEEE International Conference on Robotics and Automation (ICRA) 2021. 43 AB3DMOT+PointRCNN code 37.81 % 32.37 % 44.33 % 34.91 % 59.35 % 48.44 % 62.83 % 71.31 % 38.13 % X. Weng, J. Wang, D. Held and K. Kitani: 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics. IROS 2020. 44 NOMT 36.26 % 31.87 % 41.63 % 34.61 % 61.26 % 46.88 % 62.25 % 72.83 % 36.52 % W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. ICCV 2015. 45 NOMT-HM* 34.76 % 33.96 % 35.81 % 37.64 % 62.76 % 39.23 % 66.66 % 75.36 % 38.60 % W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. ICCV 2015. 46 SCEA* 34.66 % 34.31 % 35.21 % 36.70 % 67.74 % 38.33 % 69.46 % 76.23 % 43.26 % J. Yoon, C. Lee, M. Yang and K. Yoon: Online Multi-object Tracking via Structural Constraint Event Aggregation. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2016. 47 JRMOT code 34.24 % 38.79 % 30.55 % 42.51 % 66.64 % 32.69 % 70.12 % 76.64 % 45.31 % A. Shenoi, M. Patel, J. Gwak, P. Goebel, A. Sadeghian, H. Rezatofighi, R. Mart\'in-Mart\'in and S. Savarese: JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset. The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020. 48 RMOT 34.09 % 29.61 % 39.45 % 32.12 % 60.92 % 43.14 % 63.59 % 72.99 % 34.05 % J. Yoon, M. Yang, J. Lim and K. Yoon: Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects. IEEE Winter Conference on Applications of Computer Vision (WACV) 2015. 49 CIWT* code 33.93 % 34.00 % 34.07 % 36.35 % 67.44 % 36.34 % 70.76 % 75.96 % 42.10 % A. Osep, W. Mehner, M. Mathias and B. Leibe: Combined Image- and World-Space Tracking in Traffic Scenes. ICRA 2017. 50 LP-SSVM* 33.74 % 35.74 % 32.03 % 39.54 % 63.11 % 36.36 % 63.24 % 75.18 % 43.42 % S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. International Journal of Computer Vision 2016. 51 MCMOT-CPD 32.06 % 36.30 % 28.83 % 39.06 % 68.00 % 32.14 % 69.60 % 76.67 % 44.19 % B. Lee, E. Erdenee, S. Jin, M. Nam, Y. Jung and P. Rhee: Multi-class Multi-object Tracking Using Changing Point Detection. ECCVWORK 2016. 52 Decoupled DeepSORT 32.02 % 34.10 % 30.43 % 39.37 % 56.88 % 38.34 % 46.56 % 73.36 % 34.12 % 53 NOMT-HM 31.13 % 25.64 % 38.23 % 27.75 % 59.78 % 42.38 % 65.06 % 72.90 % 26.86 % W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. ICCV 2015. 54 LP-SSVM 28.19 % 29.29 % 27.57 % 31.61 % 60.77 % 31.12 % 61.78 % 72.49 % 32.42 % S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. International Journal of Computer Vision 2016. 55 SCEA 27.80 % 27.41 % 28.61 % 29.38 % 62.30 % 30.44 % 68.62 % 73.55 % 31.75 % J. Yoon, C. Lee, M. Yang and K. Yoon: Online Multi-object Tracking via Structural Constraint Event Aggregation. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2016. 56 CEM code 25.83 % 25.54 % 26.41 % 27.54 % 60.66 % 27.91 % 68.34 % 73.43 % 26.59 % A. Milan, S. Roth and K. Schindler: Continuous Energy Minimization for Multitarget Tracking. IEEE TPAMI 2014. 57 Complexer-YOLO 14.08 % 24.91 % 8.15 % 27.21 % 52.62 % 8.63 % 59.39 % 68.64 % 11.99 % M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019. Table as LaTeX | Only published MethodsRelated DatasetsTUD Datasets: "TUD Multiview Pedestrians" and "TUD Stadmitte" Datasets.PETS 2009: The Datasets for the "Performance Evaluation of Tracking and Surveillance"" Workshop.EPFL Terrace: Multi-camera pedestrian videos.ETHZ Sequences: Inner City Sequences from Mobile Platforms. 2b1af7f3a8