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MMOT: Motion-Aware Multi-Object Tracking with Optical Flow

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Published:22 May 2023Publication History

ABSTRACT

Modern multi-object tracking (MOT) benefited from recent advances in deep neural network and large video datasets. However, there are still some challenges impeding further improvement of the tracking performance, including complex background, fast motion and occlusion scenes. In this paper, we propose a new framework which employs motion information with optical flow, enable directly distinguishing the foreground and background regions. The proposed end-to-end network consists of two branches to separately model the spatial feature representations and optical flow motion patterns. We propose different fusion mechanism by combining the motion clues and appearance information. The results on MOT17 dataset show that our method is an effective mechanism in modeling temporal-spatial information.

References

  1. Tak-Wai Hui, Xiaoou Tang, Chen Change Loy. 2018.LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation.In Computer Vision & Pattern Recognition.Google ScholarGoogle Scholar
  2. J. Peng, C. Wang, F. Wan, Y. Wu, Y. Wang, Y. Tai, C. Wang, J. Li, F. Huang, and Y. Fu.2020. Chained-tracker: Chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking. ArXiv preprint arXiv:2007.14557, 2020Google ScholarGoogle Scholar
  3. A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft. 2016. Simple online and realtime tracking. In ICIP. IEEE, pp. 3464–3468.Google ScholarGoogle Scholar
  4. Wojke, N., Bewley, A., Paulus, D. 2017. Simple online and realtime tracking with a deep association metric. In: 2017 IEEE international conference on image processing (ICIP). pp. 3645–3649.Google ScholarGoogle Scholar
  5. Zhou, X., Koltun, V., Kr¨ahenb¨uhl, P.2020. Tracking objects as points. In European Conference on Computer Vision. pp. 474–490. Springer.Google ScholarGoogle Scholar
  6. Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.2021.Fairmot: On the fairness of detection and re-identification in multiple object tracking. International Journal of Computer Vision 129(11), 3069–3087Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. Peng, C. Wang, F. Wan, Y. Wu, Y. Wang, Y. Tai, C. Wang, J. Li, F. Huang, and Y. Fu.2020. Chained-tracker: Chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking. arXiv preprint arXiv:2007.14557.Google ScholarGoogle Scholar
  8. B. Pang, Y. Li, Y. Zhang, M. Li, and C. Lu. 2020. Tubetk: Adopting tubes to track multi-object in a one-step training model. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6308–6318.Google ScholarGoogle Scholar
  9. Xu, Y., Ban, Y., Delorme, G., Gan, C., Rus, D., Alameda-Pineda, X.2021. Transcenter: Transformers with dense queries for multiple-object tracking. In arXiv preprint arXiv:2103.15145Google ScholarGoogle Scholar
  10. G. Welch, G. Bishop 1995. An introduction to the kalman filter.Google ScholarGoogle Scholar
  11. E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox.2017. FlowNet2.0: Evolution of optical flow estimation with deep networks. CVPR, pages 2462–2470Google ScholarGoogle Scholar
  12. Saad A. Yaseen and Sreela Sasi. 2014. Robust Algorithm for Object Detection and Tracking in a Dynamic Scene. In Journal of Image and Graphics, Vol. 2, No. 1, pp. 41- 45, June 2014. doi: 10.12720/joig.2.1.41-45Google ScholarGoogle ScholarCross RefCross Ref
  13. K. He, X. Zhang, S. Ren, and J. Sun.2016. Deep residual learning for image recognition. In CVPR. pp. 770–778.Google ScholarGoogle Scholar
  14. S. Shao, Z. Zhao, B. Li, T. Xiao, G. Yu, X. Zhang, and J. Sun.2018. Crowdhuman: A benchmark for detecting human in a crowd.In arXiv preprint arXiv:1805.00123Google ScholarGoogle Scholar
  15. P. Dollar, C. Wojek, B. Schiele, and P. Perona.2009. Pedestrian detection: A ´ benchmark. In CVPR. IEEE, pp. 304–311.Google ScholarGoogle Scholar
  16. K. Bernardin and R. Stiefelhagen, 2008 Evaluating multiple object tracking performance: the clear mot metrics.EURASIP Journal on Image and Video Processing, vol. 2008, pp. 1–10Google ScholarGoogle Scholar
  17. Karthik Dinesh and Sumana Gupta. 2014. Video Stabilization, Camera Motion Pattern Recognition and Motion Tracking Using Spatiotemporal Regularity Flow.In Journal of Image and Graphics, Vol. 2, No. 1, pp. 33-40, June 2014. doi: 10.12720/joig.2.1.33-40Google ScholarGoogle ScholarCross RefCross Ref

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      ICCPR '22: Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition
      November 2022
      683 pages
      ISBN:9781450397056
      DOI:10.1145/3581807

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      Publication History

      • Published: 22 May 2023

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