Advertisement

Towards Real-Time Multi-Object Tracking

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12356)

Abstract

Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection paradigm. It has 1) a detection model for target localization and 2) an appearance embedding model for data association. Having the two models separately executed might lead to efficiency problems, as the running time is simply a sum of the two steps without investigating potential structures that can be shared between them. Existing research efforts on real-time MOT usually focus on the association step, so they are essentially real-time association methods but not real-time MOT system. In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model. Specifically, we incorporate the appearance embedding model into a single-shot detector, such that the model can simultaneously output detections and the corresponding embeddings. We further propose a simple and fast association method that works in conjunction with the joint model. In both components the computation cost is significantly reduced compared with former MOT systems, resulting in a neat and fast baseline for future follow-ups on real-time MOT algorithm design. To our knowledge, this work reports the first (near) real-time MOT system, with a running speed of 22 to 40 FPS depending on the input resolution. Meanwhile, its tracking accuracy is comparable to the state-of-the-art trackers embodying separate detection and embedding (SDE) learning (\(64.4\%\) MOTA v.s. \(66.1\%\) MOTA on MOT-16 challenge). Code and models are available at https://github.com/Zhongdao/Towards-Realtime-MOT.

Keyword

Multi-Object Tracking 

References

  1. 1.
    Bergmann, P., Meinhardt, T., Leal-Taixe, L.: Tracking without bells and whistles. arXiv preprint arXiv:1903.05625 (2019)
  2. 2.
    Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. J. Image Video Process. 2008, 1 (2008)CrossRefGoogle Scholar
  3. 3.
    Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: ICIP (2016)Google Scholar
  4. 4.
    Brasó, G., Leal-Taixé, L.: Learning a neural solver for multiple object tracking. In: CVPR (2020)Google Scholar
  5. 5.
    Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: CVPR (2018)Google Scholar
  6. 6.
    Choi, W.: Near-online multi-target tracking with aggregated local flow descriptor. In: ICCV (2015)Google Scholar
  7. 7.
    Dendorfer, P., et al.: CVPR19 tracking and detection challenge: how crowded can it get? arXiv preprint arXiv:1906.04567 (2019)
  8. 8.
    Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In: CVPR (2009)Google Scholar
  9. 9.
    Ess, A., Leibe, B., Schindler, K., van Gool, L.: A mobile vision system for robust multi-person tracking. In: CVPR (2008)Google Scholar
  10. 10.
    Fang, K., Xiang, Y., Li, X., Savarese, S.: Recurrent autoregressive networks for online multi-object tracking. In: WACV (2018)Google Scholar
  11. 11.
    Girshick, R.: Fast R-CNN. In: ICCV (2015)Google Scholar
  12. 12.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)Google Scholar
  13. 13.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  14. 14.
    Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)
  15. 15.
    Jiang, H., Fels, S., Little, J.J.: A linear programming approach for multiple object tracking. In: CVPR (2007)Google Scholar
  16. 16.
    Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: CVPR (2018)Google Scholar
  17. 17.
    Kim, C., Li, F., Ciptadi, A., Rehg, J.M.: Multiple hypothesis tracking revisited. In: ICCV (2015)Google Scholar
  18. 18.
    Law, H., Deng, J.: CornerNet: detecting objects as paired keypoints. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 765–781. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01264-9_45CrossRefGoogle Scholar
  19. 19.
    Leal-Taixé, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., Savarese, S.: Learning an image-based motion context for multiple people tracking. In: CVPR (2014)Google Scholar
  20. 20.
    Li, J., Gao, X., Jiang, T.: Graph networks for multiple object tracking. In: CVPR (2020)Google Scholar
  21. 21.
    Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR (2017)Google Scholar
  22. 22.
    Liu, Y., Yan, J., Ouyang, W.: Quality aware network for set to set recognition. In: CVPR (2017)Google Scholar
  23. 23.
    Mahmoudi, N., Ahadi, S.M., Rahmati, M.: Multi-target tracking using CNN-based features: CNNMTT. Multimed. Tools Appl. 78(6), 7077–7096 (2019)CrossRefGoogle Scholar
  24. 24.
    Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K.: MOT16: a benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831 (2016)
  25. 25.
    Newell, A., Huang, Z., Deng, J.: Associative embedding: end-to-end learning for joint detection and grouping. In: NIPS (2017)Google Scholar
  26. 26.
    Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: CVPR (2011)Google Scholar
  27. 27.
    Redmon, J., Farhadi, A.: YOLOV3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
  28. 28.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)Google Scholar
  29. 29.
    Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: CVPR (2015)Google Scholar
  30. 30.
    Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: NIPS (2018)Google Scholar
  31. 31.
    Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: NIPS (2016)Google Scholar
  32. 32.
    Sun, S., Akhtar, N., Song, H., Mian, A.S., Shah, M.: Deep affinity network for multiple object tracking. IEEE Trans. Pattern Anal. Mach. Intell. (2019)Google Scholar
  33. 33.
    Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501–518. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01225-0_30CrossRefGoogle Scholar
  34. 34.
    Szegedy, C., et al.: Going deeper with convolutions. In: CVPR (2015)Google Scholar
  35. 35.
    Tang, S., Andriluka, M., Andres, B., Schiele, B.: Multiple people tracking by lifted multicut and person re-identification. In: CVPR (2017)Google Scholar
  36. 36.
    Voigtlaender, P., et al.: Mots: Multi-object tracking and segmentation. In: CVPR (2019)Google Scholar
  37. 37.
    Wen, L., Li, W., Yan, J., Lei, Z., Yi, D., Li, S.Z.: Multiple target tracking based on undirected hierarchical relation hypergraph. In: CVPR (2014)Google Scholar
  38. 38.
    Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: ICIP (2017)Google Scholar
  39. 39.
    Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search. In: CVPR (2017)Google Scholar
  40. 40.
    Yu, F., Li, W., Li, Q., Liu, Yu., Shi, X., Yan, J.: POI: multiple object tracking with high performance detection and appearance feature. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 36–42. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-48881-3_3CrossRefGoogle Scholar
  41. 41.
    Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
  42. 42.
    Roshan Zamir, A., Dehghan, A., Shah, M.: GMCP-tracker: global multi-object tracking using generalized minimum clique graphs. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7573, pp. 343–356. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33709-3_25CrossRefGoogle Scholar
  43. 43.
    Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: CVPR (2008)Google Scholar
  44. 44.
    Zhang, S., Benenson, R., Schiele, B.: Citypersons: a diverse dataset for pedestrian detection. In: CVPR (2017)Google Scholar
  45. 45.
    Zheng, L., et al.: MARS: a video benchmark for large-scale person re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 868–884. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46466-4_52CrossRefGoogle Scholar
  46. 46.
    Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: Past, present and future. arXiv preprint arXiv:1610.02984 (2016)
  47. 47.
    Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., Tian, Q.: Person re-identification in the wild. In: CVPR (2017)Google Scholar
  48. 48.
    Zhou, Z., Xing, J., Zhang, M., Hu, W.: Online multi-target tracking with tensor-based high-order graph matching. In: ICPR (2018)Google Scholar
  49. 49.
    Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., Yang, M.-H.: Online multi-object tracking with dual matching attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 379–396. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01228-1_23CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  2. 2.Australian National UniversityCanberraAustralia

Personalised recommendations