Abstract
State-of-the-art multiple object tracking and segmentation methods predict a pixel-wise segmentation mask for each detected instance object. Such methods are sensitive to the inaccurate detection and suffer from heavy computational overhead. Besides, when associating pixel-wise masks, additional optical flow networks are required to assist in mask propagation. To relieve these three issues, we present PolyTracker, which adopts object contour, in the form of a vertex sequence along with the object silhouette, as an alternative representation of the pixel-wise segmentation mask. In the PolyTracker, we design an effective contour deformation module based on an iterative and progressive mechanism, which is robust to the inaccurate detection and has low model complexity. Furthermore, benefiting from the powerful contour deformation module, we design a novel data association method, which achieves effective contour propagation by fully mining contextual cues from contours. Since data association relies heavily on pedestrian appearance representation, we design a Reliable Pedestrian Information Aggregation (RPIA) module to fully exploit the discriminative re-identification feature. Extensive experiments demonstrate that our PolyTracker sets the promising records on the MOTS20 benchmark.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahrnbom, M., Nilsson, M.G., Ardö, H.: Real-time and online segmentation multi-target tracking with track revival re-identification. In: VISIGRAPP, pp. 777–784 (2021)
Bergmann, P., Meinhardt, T., Leal-Taixe, L.: Tracking without bells and whistles. In: IEEE International Conference on Computer Vision (ICCV), pp. 941–951 (2019)
Evangelidis, G.D., Psarakis, E.Z.: Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 30(10), 1858–1865 (2008)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: IEEE International Conference on Computer Vision (ICCV), pp. 2961–2969 (2017)
Hu, A., Kendall, A., Cipolla, R.: Learning a spatio-temporal embedding for video instance segmentation. arXiv preprint arXiv:1912.08969 (2019)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision (IJCV) 1(4), 321–331 (1988)
Kim, C., Fuxin, L., Alotaibi, M., Rehg, J.M.: Discriminative appearance modeling with multi-track pooling for real-time multi-object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9553–9562 (2021)
Ling, H., Gao, J., Kar, A., Chen, W., Fidler, S.: Fast interactive object annotation with curve-GCN. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5257–5266 (2019)
Pang, J., et al.: Quasi-dense similarity learning for multiple object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 164–173 (2021)
Peng, S., Jiang, W., Pi, H., Li, X., Bao, H., Zhou, X.: Deep snake for real-time instance segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8533–8542 (2020)
Porzi, L., Hofinger, M., Ruiz, I., Serrat, J., Bulo, S.R., Kontschieder, P.: Learning multi-object tracking and segmentation from automatic annotations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6846–6855 (2020)
Saleh, F., Aliakbarian, S., Rezatofighi, H., Salzmann, M., Gould, S.: Probabilistic tracklet scoring and inpainting for multiple object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14329–14339 (2021)
Shuai, B., Berneshawi, A., Li, X., Modolo, D., Tighe, J.: SiamMOT: Siamese multi-object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12372–12382 (2021)
Voigtlaender, P., et al.: MOTS: multi-object tracking and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7942–7951 (2019)
Xie, E., et al.: PolarMask: single shot instance segmentation with polar representation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12193–12202 (2020)
Xie, E., Wang, W., Ding, M., Zhang, R., Luo, P.: PolarMask++: enhanced polar representation for single-shot instance segmentation and beyond. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 44, 5385–5400 (2021)
Xu, Z., Meng, A., Shi, Z., Yang, W., Chen, Z., Huang, L.: Continuous copy-paste for one-stage multi-object tracking and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15323–15332 (2021)
Xu, Z., et al.: Segment as points for efficient online multi-object tracking and segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 264–281. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_16
Xu, Z., et al.: Pointtrack++ for effective online multi-object tracking and segmentation. arXiv preprint arXiv:2007.01549 (2020)
Yang, F., et al.: ReMOTS: self-supervised refining multi-object tracking and segmentation. arXiv preprint arXiv:2007.03200 (2020)
Yu, F., Wang, D., Shelhamer, E., Darrell, T.: Deep layer aggregation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2403–2412 (2018)
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 (IJCV) 129(11), 3069–3087 (2021)
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)
Aknowledgement
This work was supported by the National Natural Science Foundation of China under Contract 62021001.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shen, S., Feng, H., Zhou, W., Li, H. (2022). PolyTracker: Progressive Contour Regression for Multiple Object Tracking and Segmentation. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_50
Download citation
DOI: https://doi.org/10.1007/978-3-031-18916-6_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18915-9
Online ISBN: 978-3-031-18916-6
eBook Packages: Computer ScienceComputer Science (R0)