Abstract
Multiple Object Tracking (MOT) consists of two components: detection and data association. In the popular tracking-by-detection models, these two components are separate: all objects of interest in a frame are detected first, and then associated with the objects in tracked queues using intersection-over-union (IOU) of bounding box and/or appearance matching. Appearance feature (ID embedding) can be extracted from a separate re-identification model or from a joint model with the detection network. In this paper, a joint detection and tracking model with Siamese structure is proposed for MOT. We apply CenterNet, an anchor free object detector for detection. Motion prediction and appearance matching are implemented with the network for association. Experimental results demonstrate the effectiveness of our model. State-of-the-art MOTA of 69.9% on MOT20 is achieved.
This work is supported by the National Natural Science Foundation of China under Project 61175116.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bergmann, P., Meinhardt, T., Leal-Taixe, L.: Tracking without bells and whistles. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 941–951 (2019)
Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468. IEEE (2016)
Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2017)
Chaabane, M., Zhang, P., Beveridge, J.R., et al.: DEFT: detection embeddings for tracking. arXiv:2102.02267v2 (2021)
Ciaparrone, G., Sánchez, F.L., Tabik, S., Troiano, L., Tagliaferri, R., Herrera, F.: Deep learning in video multi-object tracking: a survey. Neurocomputing 381, 61–88 (2020)
Feichtenhofer, C., Pinz, A., Zisserman, A.: Detect to track and track to detect. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3038–3046 (2017)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: FlowNet 2.0: evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2462–2470 (2017)
Liang, C., et al.: Rethinking the competition between detection and ReID in multi-object tracking. arXiv preprint arXiv:2010.12138 (2020)
Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., Kim, T.K.: Multiple object tracking: a literature review. Artif. Intell. 293, 103448 (2020)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)
Shuai, B., Berneshawi, A.G., Modolo, D., Tighe, J.: Multi-object tracking with siamese track-RCNN. arXiv preprint arXiv:2004.07786 (2020)
Sun, P., et al.: TransTrack: multiple-object tracking with transformer. arXiv preprint arXiv:2012.15460 (2020)
Sun, S., Akhtar, N., Song, H., Mian, A., Shah, M.: Deep affinity network for multiple object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 104–119 (2019)
Wang, Y., Kitani, K., Weng, X.: Joint object detection and multi-object tracking with graph neural networks. arXiv preprint arXiv:2006.13164 (2020)
Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649. IEEE (2017)
Xu, Y., Ban, Y., Delorme, G., Gan, C., Rus, D., Alameda-Pineda, X.: TransCenter: transformers with dense queries for multiple-object tracking. arXiv preprint arXiv:2103.15145 (2021)
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. Vis. 129, 3069–3087 (2021). https://doi.org/10.1007/s11263-021-01513-4
Zhou, X., Koltun, V., Krähenbühl, P.: Tracking objects as points. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 474–490. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_28
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Qin, J., Huang, C., Xu, J. (2021). End-to-End Multiple Object Tracking with Siamese Networks. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_65
Download citation
DOI: https://doi.org/10.1007/978-3-030-92310-5_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92309-9
Online ISBN: 978-3-030-92310-5
eBook Packages: Computer ScienceComputer Science (R0)