Multi-object tracking with dynamic weighted appearance evolution and local matching

Xiangbin Shi; Jiaming Liu; Lin Li; Deyuan Zhang

doi:10.1117/12.3034199

13 June 2024 Multi-object tracking with dynamic weighted appearance evolution and local matching

Xiangbin Shi, Jiaming Liu, Lin Li, Deyuan Zhang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131803L (2024) https://doi.org/10.1117/12.3034199
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

This study addresses the multi-object tracking challenges in long-term video surveillance scenarios. In such scenarios, pedestrian targets often face extended temporal spans, occlusions, background noise, and non-linear illumination changes. These challenges make it difficult for conventional pedestrian re-identification algorithms to effectively capture their appearance features. To tackle these issues, this paper proposes the method called dynamic weighted appearance evolution and local matching. It utilizes a dynamic weighted appearance evolution mechanism, addressing the problem of preserving only short-term features of targets in traditional feature lists and mitigating excessive noise interference in trajectory features when detection quality is limited. In the matching stage, a strategy employing local features to assist global features in re-identification is utilized. Precise matching of local features enables better handling of challenges such as target occlusion and non-linear illumination changes. Finally, the logic for updating trajectory appearance features is refined, extracting effective local features from lower-quality detections to precisely update trajectories, achieving more accurate tracking results. Experimental evaluation shows that our approach reduces the occurrence of ID switching and is useful for tracking reliability in the complex case of long videos.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangbin Shi, Jiaming Liu, Lin Li, and Deyuan Zhang "Multi-object tracking with dynamic weighted appearance evolution and local matching", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131803L (13 June 2024); https://doi.org/10.1117/12.3034199

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KEYWORDS

Detection and tracking algorithms

Light sources and illumination

Video surveillance

Feature extraction

Target detection

Video

Background noise

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