Prof. Zongyao Sha Profile

Prof. Zongyao Sha

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Proceedings Article | 13 June 2024 Paper

CSVD: a cross-scenario vehicle dataset for multi-object tracking

Xiaolei Li, Juefan Zhou, Xingjie Xiao, Jiayu Lin, Siyuan Yang, Zongyao Sha, Jianguang Tu

Proceedings Volume 13180, 131804C (2024) https://doi.org/10.1117/12.3033630

KEYWORDS: Object detection, Detection and tracking algorithms, Unmanned aerial vehicles, Video, Target detection

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Leveraging visual sensing technologies for the detection and tracking of vehicles represents a critical application domain for unmanned aerial vehicles (UAVs), notably in challenging operational contexts. This study focuses on enhancing UAV functionalities in intricate environments through the development of a specialized dataset, derived from battlefield scenarios, to facilitate advanced research on vehicle detection and multi-target tracking under complex conditions. A comprehensive collection of vehicular movement videos spanning diverse scenarios was amassed and manually annotated, culminating in the creation of the " Cross-Scenario Vehicle Detection" (CSVD) dataset. This dataset encompasses a wide array of environmental settings, featuring urban landscapes, plains, and forests, across the four seasons, resulting in a total of 13,025 meticulously annotated images. Utilizing several state-of-the-art deep learning models, we established robust benchmarks for object detection. Additionally, an extensive evaluation and performance validation were conducted using cutting-edge multi-object tracking algorithms on the CSVD dataset, incorporating diverse assessment metrics. The conducted experiments demonstrate the dataset's robust applicability and versatility, endorsing its effectiveness for the development and evaluation of UAV-based vehicle detection and multi-target tracking systems in complex settings.

Proceedings Article | 27 March 2024 Paper

Multi-person tracking based on spatial location constraint and occlusion awareness

Xiaolei Li, Jiayu Lin, Zongyao Sha, Jianguang Tu

Proceedings Volume 13105, 131051P (2024) https://doi.org/10.1117/12.3026428

KEYWORDS: Target detection, Detection and tracking algorithms, Video surveillance, Video

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In the task of multi-person tracking, we have observed that ID switches in applications can lead to confusion in tracking targets, significantly reducing the efficiency of target tracking in actual work and the user experience. Therefore, this paper proposes a new method for multi-person tracking, building upon the BoT-SORT, with a focus on identity switching. The proposed framework integrates spatial location constraint and occlusion awareness into the tracking process. By combining spatial location and occlusion for track initialization, and methods for ID switch verification and rectification, we have enhanced the utilization of low-confidence detections, effectively reduced ID switches in multi-person tracking. The effectiveness of the proposed method has been verified through ablation experiments and validated on the MOT20 benchmark, The HOTA increased by 0.3 to 63.6, the MOTA increased by 0.3 to 78.1, and the IDF1 increased by 0.9 to 78.4. Particularly, the number of ID switches decreased from 1313 before the improvement to 1007, a reduction of 23.3%.

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