Multi-Camera Multi-Vehicle Tracking With Domain Generalization and Contextual Constraints

Nhat Minh Chung, Huy Dinh-Anh Le, Vuong Ai Nguyen, Quang Qui-Vinh Nguyen, Thong Duy-Minh Nguyen, Tin-Trung Thái, Synh Viet-Uyen Ha; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 3327-3337

Abstract

In this paper, we develop and propose a system for Multi-Camera Multi-Target (MCMT) Vehicle Tracking in Track 1 of AI City Challenge 2022. There are many technical difficulties to the MCMT problem such as a common lack of labelled data in real scenarios, a distortion of vehicle detailed appearances in recording, and ambiguity between highly similar vehicles. Taking those into account, we develop a 3-component MCMT system that exploits vehicle behavior, leverages synthetic data and augmentation techniques to exploit as much labeled data as possible, and enforce contextual constraints to address ambiguity in terms of vehicle appearances. Specifically, our system involves a motion-driven vehicle tracker, applying MixStyle domain generalization on the TransReID model, and experiment with various constraints such as neighbour matching.

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[bibtex]
@InProceedings{Chung_2022_CVPR, author = {Chung, Nhat Minh and Le, Huy Dinh-Anh and Nguyen, Vuong Ai and Nguyen, Quang Qui-Vinh and Nguyen, Thong Duy-Minh and Th\'ai, Tin-Trung and Ha, Synh Viet-Uyen}, title = {Multi-Camera Multi-Vehicle Tracking With Domain Generalization and Contextual Constraints}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {3327-3337} }