NAVE	Networked Augmented Virtual Environment (NAVE) Group

Publication:Xin Zhang, Song Gao, Yuanzhe Yang, Chengxiang Chu and Zhou Zhou. Head Point Positioning and Spatial-Channel Self-Attention Network for Multi-Object Tracking[C].2022 26th International Conference on Pattern Recognition (ICPR), Montreal, Canada, 21-25 August 2022. (CCF-C conference) pdf         Multi-Object Tracking (MOT) aims to generate trajectories for multiple objects in the surveillance scene. This is a challenging task because the pedestrians in tracking video often gather together and occlude each other. Consequently, the two main problems in the popular tracking-by-detection framework are how to alleviate unreliable detection and extract robust object appearance features. In this paper, we propose a new tracking method that is composed of two novel types of modules - an object detection strategy based on pedestrian head point positioning and a Spatial-Channel Self-Attention feature extraction network (SCSAN). Specifically, the proposed detection strategy generates more accurate tracking object bounding boxes with Soft-Head-NMS, which combines the advantages of object detection and head point positioning. The head point location information is used as a guidance to screen unreliable detection. The SCSAN utilizes the Spatial-Channel Self-Attention mechanism to lead and determine the optimal attention value for each area and channel. Extensive experiments are carried out to demonstrate the proposed tracker achieves competitive results and is state-of-the-art in half metrics. create by admin at 2022-12-19 18:55:32