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SOMPT22: A Surveillance Oriented Multi-pedestrian Tracking Dataset

  • Conference paper
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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13805))

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Abstract

Multi-object tracking (MOT) has been dominated by the use of track by detection approaches due to the success of convolutional neural networks (CNNs) on detection in the last decade. As the datasets and bench-marking sites are published, research direction has shifted towards yielding best accuracy on generic scenarios including re-identification (reID) of objects while tracking. In this study, we narrow the scope of MOT for surveillance by providing a dedicated dataset of pedestrians and focus on in-depth analyses of well performing multi-object trackers to observe the weak and strong sides of state-of-the-art (SOTA) techniques for real-world applications. For this purpose, we introduce SOMPT22 dataset; a new set for multi person tracking with annotated short videos captured from static cameras located on poles with 6-8 m in height positioned for city surveillance. This provides a more focused and specific benchmarking of MOT for outdoor surveillance compared to public MOT datasets. We analyze MOT trackers classified as one-shot and two-stage with respect to the way of use of detection and reID networks on this new dataset. The experimental results of our new dataset indicate that SOTA is still far from high efficiency, and single-shot trackers are good candidates to unify fast execution and accuracy with competitive performance. The dataset will be available at: sompt22.github.io.

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References

  1. Alberola-López, C., Casar-Corredera, J.R., Ruiz-Alzola, J.: A comparison of CFAR strategies for blob detection in textured images. In: 1996 8th European Signal Processing Conference (EUSIPCO 1996), pp. 1–4 (1996)

    Google Scholar 

  2. Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE (2016). https://doi.org/10.1109/icip.2016.7533003

  3. Braun, M., Krebs, S., Flohr, F., Gavrila, D.M.: EuroCity persons: a novel benchmark for person detection in traffic scenes. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1844–1861 (2019). https://doi.org/10.1109/tpami.2019.2897684

    Article  Google Scholar 

  4. Broström, M.: Real-time multi-object tracker using YOLOv5 and deep sort with OSNet (2022). https://github.com/mikel-brostrom/Yolov5_DeepSort_OSNet

  5. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005). https://doi.org/10.1109/CVPR.2005.177

  6. Dendorfer, P., et al.: MOT20: a benchmark for multi object tracking in crowded scenes. arXiv:2003.09003 (2020). http://arxiv.org/abs/1906.04567

  7. Dendorfer, P., et al.: Motchallenge: a benchmark for single-camera multiple target tracking. Int. J. Comput. Vis. 129(4), 845–881 (2020)

    Article  Google Scholar 

  8. Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012). https://doi.org/10.1109/TPAMI.2011.155

    Article  Google Scholar 

  9. Du, Y., Song, Y., Yang, B., Zhao, Y.: Strongsort: make deepsort great again (2022). https://doi.org/10.48550/arxiv.2202.13514

  10. Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: survey and experiments. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2179–2195 (2009). https://doi.org/10.1109/TPAMI.2008.260

    Article  Google Scholar 

  11. Ferryman, J., Shahrokni, A.: Pets 2009: dataset and challenge (2009)

    Google Scholar 

  12. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2012)

    Google Scholar 

  13. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015). https://doi.org/10.1109/TPAMI.2014.2345390

    Article  Google Scholar 

  14. INTEL: Cvat. https://openvinotoolkit.github.io/cvat/docs/

  15. Jabar, F., Farokhi, S., Sheikh, U.U.: Object tracking using SIFT and KLT tracker for UAV-based applications. In: 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), pp. 65–68 (2015). https://doi.org/10.1109/IRIS.2015.7451588

  16. Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82, 35–45 (1960). https://doi.org/10.1109/CVPR.2005.177

    Article  MathSciNet  Google Scholar 

  17. Kuhn, H.W.: Variants of the Hungarian method for assignment problems (1956)

    Google Scholar 

  18. Leal-Taixé, L., Milan, A., Reid, I., Roth, S., Schindler, K.: MOTChallenge 2015: towards a benchmark for multi-target tracking. arXiv:1504.01942 (2015). http://arxiv.org/abs/1504.01942

  19. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark (2015)

    Google Scholar 

  20. Lin, T.Y., et al.: Microsoft coco: common objects in context (2014). https://doi.org/10.48550/arxiv.1405.0312

  21. Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  22. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)

    Article  Google Scholar 

  23. Luiten, J., et al.: HOTA: a higher order metric for evaluating multi-object tracking. Int. J. Comput. Vision 129(2), 548–578 (2020)

    Article  Google Scholar 

  24. Manen, S., Gygli, M., Dai, D., Van Gool, L.: Pathtrack: fast trajectory annotation with path supervision (2017). https://doi.org/10.48550/arxiv.1703.02437

  25. Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: multi-object tracking with transformers (2021). https://doi.org/10.48550/arxiv.2101.02702

  26. Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K.: MOT16: a benchmark for multi-object tracking. arXiv:1603.00831 (2016). http://arxiv.org/abs/1603.00831

  27. Naphade, M., et al.: The 5th AI city challenge. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2021)

    Google Scholar 

  28. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018). https://doi.org/10.48550/arxiv.1804.02767

  29. Ristani, E., Solera, F., Zou, R.S., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking (2016). https://doi.org/10.48550/arxiv.1609.01775

  30. Shao, S., et al.: Crowdhuman: a benchmark for detecting human in a crowd (2018). https://doi.org/10.48550/arxiv.1805.00123

  31. Stiefelhagen, R., Bernardin, K., Bowers, R., Rose, R., Michel, M., Garofolo, J.: The clear 2007 evaluation (2007). https://doi.org/10.1007/978-3-540-68585-2_1

  32. Sun, P., et al.: Dancetrack: multi-object tracking in uniform appearance and diverse motion (2021). https://doi.org/10.48550/arxiv.2111.14690

  33. Sun, P., et al.: Transtrack: multiple object tracking with transformer (2020). https://doi.org/10.48550/arxiv.2012.15460

  34. Vaswani, A., et al.: Attention is all you need (2017). https://doi.org/10.48550/arxiv.1706.03762

  35. Wang, X., et al.: Panda: a gigapixel-level human-centric video dataset. In: 2020 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020)

    Google Scholar 

  36. Wojek, C., Walk, S., Schiele, B.: Multi-cue onboard pedestrian detection. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 794–801 (2009). https://doi.org/10.1109/CVPR.2009.5206638

  37. Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric (2017). https://doi.org/10.48550/arxiv.1703.07402

  38. Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search (2016). https://doi.org/10.48550/arxiv.1604.01850

  39. Yu, F., et al.: BDD100K: a diverse driving dataset for heterogeneous multitask learning (2018). https://doi.org/10.48550/arxiv.1805.04687

  40. Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., Wei, Y.: MOTR: end-to-end multiple-object tracking with transformer (2021). https://doi.org/10.48550/arxiv.2105.03247

  41. Zhang, S., Benenson, R., Schiele, B.: Citypersons: a diverse dataset for pedestrian detection (2017). https://doi.org/10.48550/arxiv.1702.05693

  42. Zhang, S., Xie, Y., Wan, J., Xia, H., Li, S.Z., Guo, G.: Widerperson: a diverse dataset for dense pedestrian detection in the wild (2019). https://doi.org/10.48550/arxiv.1909.12118

  43. Zhang, Y., et al.: Bytetrack: multi-object tracking by associating every detection box (2021). https://doi.org/10.48550/arxiv.2110.06864

  44. Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: FairMOT: on the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vision 129(11), 3069–3087 (2021). https://doi.org/10.1007/s11263-021-01513-4

    Article  Google Scholar 

  45. Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., Tian, Q.: Person re-identification in the wild (2016). https://doi.org/10.48550/arxiv.1604.02531

  46. Zhou, B., Bose, N.: An efficient algorithm for data association in multitarget tracking. IEEE Trans. Aerosp. Electron. Syst. 31(1), 458–468 (1995). https://doi.org/10.1109/7.366327

    Article  Google Scholar 

  47. Zhou, D., Zhang, H.: Modified GMM background modeling and optical flow for detection of moving objects. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2224–2229 (2005). https://doi.org/10.1109/ICSMC.2005.1571479

  48. Zhou, X., Koltun, V., Krähenbühl, P.: Tracking objects as points (2020). https://doi.org/10.48550/arxiv.2004.01177

  49. Zhou, X., Wang, D., Krähenbühl, P.: Objects as points (2019). https://doi.org/10.48550/arxiv.1904.07850

  50. Zhu, P., et al.: Detection and tracking meet drones challenge. IEEE Trans. Pattern Anal. Mach. Intell. (2021). https://doi.org/10.1109/TPAMI.2021.3119563

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Authors and Affiliations

  1. Aselsan Inc., Ankara, Turkey

    Fatih Emre Simsek & Cevahir Cigla

  2. Gebze Technical University, Kocaeli, Turkey

    Fatih Emre Simsek & Koray Kayabol

Authors
  1. Fatih Emre Simsek
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  2. Cevahir Cigla
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  3. Koray Kayabol
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Corresponding author

Correspondence to Fatih Emre Simsek .

Editor information

Editors and Affiliations

  1. IBM Research AI and MIT-IBM Watson AI Lab, Haifa, Israel

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Simsek, F.E., Cigla, C., Kayabol, K. (2023). SOMPT22: A Surveillance Oriented Multi-pedestrian Tracking Dataset. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13805. Springer, Cham. https://doi.org/10.1007/978-3-031-25072-9_44

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  • DOI: https://doi.org/10.1007/978-3-031-25072-9_44

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-25072-9

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