Multi-object tracking based on attention networks for Smart City system. (August 2022)

• Record Type: Journal Article
• Authors: Zhou, Xiaolong; Jia, Yangwei; Bai, Cong; Zhu, Haiping; Chan, Sixian
• Is Part Of: Sustainable energy technologies and assessments. Volume 52:Part C(2022)
• Publication Date: 2022-08
• Subjects: Multi-object tracking -- One-shot -- Anchor-free -- Real-time -- Smart City; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102216
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: The Smart City is a hot topic at present. Pedestrian tracking and behavior analysis play an important role in Smart City system. Therefore, it is urgent to design an universal and robust multi-object tracking(MOT) algorithm. MOT is a challenging problem with numerous significant applications, including robot navigation, autonomous driving and pedestrian behavior analysis. Among the existing MOT approaches, MOT models which joint learning detection and Re-ID are attracting increasing attention. Subsequently, there is competition between these two tasks jointly in a network inevitably leading to performance degradation. To address these difficulties, we propose an adaptive joint learning approach, called UnionTrack, for MOT in this paper. It handles with the problem of uniform learning between tasks in online MOT system. Firstly, UnionTrack introduces a scale-aware attention network that aggregates features from different scales for object localization and learning of the corresponding discriminative embeddings. Secondly, a cross-correlation module is designed to construct task-relevant feature maps and interact adaptively during training detection and Re-ID. Then, we perform inter-frame correlation at different stages by using high and low confidence detection to avoid occlusions and backgrounds. UnionTrack is trained in an end-to-end manner to better accommodate the training between detection and Re-ID tasks. Finally, our proposed UnionTrack is compared with state-of-the-art methods in extensively experiments and detailed performance analysis is performed in MOT15, MOT16, MOT17 and MOT20 which are all urban scene to demonstrate the effectiveness of our proposed method. Looking forward to playing a role in the Smart City systems.