Top

Published in:

2024 | OriginalPaper | Chapter

Block-Matching Multi-pedestrian Tracking

Author : Chao Zhang

Published in: Neural Information Processing

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Target association is an extremely important problem in the field of multi-object tracking, especially for pedestrian scenes with high similarity in appearance and dense distribution. The traditional approach of combining IOU and ReID techniques with the Hungarian algorithm only partially addresses these challenges. To improve the model’s matching ability, this paper proposes a block-matching model that extracts local features using a Block Matching Module (BMM) based on the Transformer model. The BMM extracts features by dividing them into blocks and mines effective features of the target to complete target similarity evaluation. Additionally, a Euclidean Distance Module (EDM) based on the Euclidean distance association matching strategy is introduced to further enhance the model’s association ability. By integrating BMM and EDM into the same multi-object tracking model, this paper establishes a novel model called BWTrack that achieves excellent performance on MOT16, MOT17, and MOT20 while maintaining high performance at 7 FPS on a single GPU.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter An Improved Target Searching and Imaging Method for CSAR

next chapter RPF3D: Range-Pillar Feature Deep Fusion 3D Detector for Autonomous Driving

Aharon, N., Orfaig, R., Bobrovsky, B.Z.: BoT-SORT: robust associations multi-pedestrian tracking. arXiv preprint arXiv:2206.14651 (2022)

Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: ICIP, pp. 3464–3468. IEEE (2016)

Cai, J., et al.: MeMOT: multi-object tracking with memory. In: CVPR, pp. 8090–8100 (2022)

Cao, J., Pang, J., Weng, X., Khirodkar, R., Kitani, K., et al.: Observation-centric sort: rethinking sort for robust multi-object tracking. arXiv preprint arXiv:2203.14360 (2022)

Chu, P., Fan, H., Tan, C.C., Ling, H.: Online multi-object tracking with instance-aware tracker and dynamic model refreshment. In: WACV, pp. 161–170. IEEE (2019)

Dendorfer, P., et al.: MOT20: a benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003 (2020)

Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

Liang, C., Zhang, Z., Zhou, X., Li, B., Zhu, S., Hu, W.: Rethinking the competition between detection and ReiD in multiobject tracking. TIP, 3182–3196 (2022)

Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K.: MOT16: a benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831 (2016)

10.

Pang, J., et al.: Quasi-dense similarity learning for multiple object tracking. In: CVPR, pp. 164–173 (2021)

11.

Peng, J., et al.: Chained-tracker: chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 145–161. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_9CrossRef

12.

Peng, J., et al.: TPM: multiple object tracking with tracklet-plane matching. Pattern Recogn. 107, 107480 (2020)

13.

Shan, C., et al.: Tracklets predicting based adaptive graph tracking. arXiv preprint arXiv:2010.09015 (2020)

14.

Sun, P., et al.: Transtrack: multiple object tracking with transformer. arXiv preprint arXiv:2012.15460 (2020)

15.

Vaswani, A., et al.: Attention is all you need. In: NeurIPS, vol. 30, pp. 6000–6010 (2017)

16.

Wang, T., et al.: Spatio-temporal point process for multiple object tracking. IEEE Trans. Neural Netw. Learn. Syst. 34(4), 1777–1788 (2023)

17.

Xu, Y., Ban, Y., Delorme, G., Gan, C., Rus, D., Alameda-Pineda, X.: TransCenter: transformers with dense queries for multiple-object tracking. arXiv e-prints, arXiv-2103 (2021)

18.

Yang, F., Odashima, S., Masui, S., Jiang, S.: Hard to track objects with irregular motions and similar appearances? Make it easier by buffering the matching space. In: WACV, pp. 4799–4808 (2023)

19.

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_3CrossRef

20.

Yu, E., Li, Z., Han, S., Wang, H.: RelationTrack: relation-aware multiple object tracking with decoupled representation. IEEE Trans. Multimedia, 2686–2697 (2021)

21.

Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., Wei, Y.: MOTR: end-to-end multiple-object tracking with transformer. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV, vol. 13687, pp. 659–675. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19812-0_38

22.

Zhang, H., et al.: ResNeSt: split-attention networks. arXiv preprint arXiv:2004.08955 (2020)

23.

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)

24.

Zhang, Y., et al.: ByteTrack: multi-object tracking by associating every detection box. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV, vol. 13682, pp. 1–21. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20047-2_1

25.

Bergmann, P., Meinhardt, T., Leal-Taixe, L.: Tracking without bells and whistles. In: ICCV, pp. 941–951 (2019)

26.

Chaabane, M., Zhang, P., Beveridge, J.R., O’Hara, S.: Deft: detection embeddings for tracking. arXiv preprint arXiv:2102.02267 (2021)

27.

Chu, P., Wang, J., You, Q., Ling, H., Liu, Z.: TransMOT: spatial-temporal graph transformer for multiple object tracking. In: WACV, pp. 4870–4880 (2023)

28.

Emami, P., Pardalos, P.M., Elefteriadou, L., Ranka, S.: Machine learning methods for data association in multi-object tracking. ACM Comput. Surv. (CSUR), 1–34 (2020)

29.

Fang, K., Xiang, Y., Li, X., Savarese, S.: Recurrent autoregressive networks for online multi-object tracking. In: WACV, pp. 466–475. IEEE (2018)

30.

Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOx: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)

31.

Han, S., Huang, P., Wang, H., Yu, E., Liu, D., Pan, X.: MAT: motion-aware multi-object tracking. Neurocomputing 476, 75–86 (2022)CrossRef

32.

He, L., Liao, X., Liu, W., Liu, X., Cheng, P., Mei, T.: FastReID: a Pytorch toolbox for general instance re-identification. arXiv preprint arXiv:2006.02631 (2020)

33.

Hyun, J., Kang, M., Wee, D., Yeung, D.Y.: Detection recovery in online multi-object tracking with sparse graph tracker. In: WACV, pp. 4850–4859 (2023)

34.

Li, W., Xiong, Y., Yang, S., Xu, M., Wang, Y., Xia, W.: Semi-TCL: semi-supervised track contrastive representation learning. arXiv preprint arXiv:2107.02396 (2021)

35.

Liang, C., Zhang, Z., Zhou, X., Li, B., Hu, W.: One more check: making “fake background” be tracked again. In: AAAI, vol. 36, pp. 1546–1554 (2022)

36.

Mahmoudi, N., Ahadi, S.M., Rahmati, M.: Multi-target tracking using CNN-based features: CNNMTT. Multimedia Tools Appl., 7077–7096 (2019)

37.

Pang, B., Li, Y., Zhang, Y., Li, M., Lu, C.: TubeTK: adopting tubes to track multi-object in a one-step training model. In: CVPR, pp. 6308–6318 (2020)

38.

Seidenschwarz, J., Braso, G., Elezi, I., Leal-Taixe, L.: Simple cues lead to a strong multi-object tracker. arXiv preprint arXiv:2206.04656 (2022)

39.

Stadler, D., Beyerer, J.: Modelling ambiguous assignments for multi-person tracking in crowds. In: WACV, pp. 133–142 (2022)

40.

Tokmakov, P., Li, J., Burgard, W., Gaidon, A.: Learning to track with object permanence. In: ICCV, pp. 10860–10869 (2021)

41.

Wang, Q., Zheng, Y., Pan, P., Xu, Y.: Multiple object tracking with correlation learning. In: CVPR, pp. 3876–3886 (2021)

42.

Wang, Y., Kitani, K., Weng, X.: Joint object detection and multi-object tracking with graph neural networks. In: ICRA, pp. 13708–13715 (2021)

43.

Wang, Z., Zheng, L., Liu, Y., Li, Y., Wang, S.: Towards real-time multi-object tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 107–122. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_7CrossRef

44.

Wojke, N., Bewley, A.: Deep cosine metric learning for person re-identification. In: WACV, pp. 748–756. IEEE (2018)

45.

Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: ICIP, pp. 3645–3649. IEEE (2017)

46.

Wu, J., Cao, J., Song, L., Wang, Y., Yang, M., Yuan, J.: Track to detect and segment: an online multi-object tracker. In: CVPR, pp. 12352–12361 (2021)

47.

Yang, F., Chang, X., Sakti, S., et al.: ReMOT: a model-agnostic refinement for multiple object tracking. Image Vis. Comput. 106, 104091 (2021)

48.

Zheng, L., Tang, M., Chen, Y., Zhu, G., Wang, J., Lu, H.: Improving multiple object tracking with single object tracking. In: CVPR, pp. 2453–2462 (2021)

49.

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_28CrossRef

Title: Block-Matching Multi-pedestrian Tracking
Author: Chao Zhang
Publisher: Springer Nature Singapore
Book: Neural Information Processing
Print ISBN: 978-981-9980-66-6

Electronic ISBN: 978-981-9980-67-3

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-99-8067-3_9

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner