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Multi-Target Multi-Camera Tracking of Vehicles using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model
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- Authors
- Hung-Min Hsu, Jiarui Cai, Yizhou Wang, Jenq-Neng Hwang, Kwang-Ju Kim
- Issue Date
- 2021-05
- Citation
- IEEE Transactions on Image Processing, v.30, pp.5198-5210
- ISSN
- 1057-7149
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- In this paper, we propose a novel framework for multi-target multi-camera tracking (MTMCT) of vehicles based on metadata-aided re-identification (MA-ReID) and the trajectory-based camera link model (TCLM). Given a video sequence and the corresponding frame-by-frame vehicle detections, we first address the isolated tracklets issue from single camera tracking (SCT) by the proposed traffic-aware single-camera tracking (TSCT). Then, after automatically constructing the TCLM, we solve MTMCT by the MA-ReID. The TCLM is generated from camera topological configuration to obtain the spatial and temporal information to improve the performance of MTMCT by reducing the candidate search of ReID. We also use the temporal attention model to create more discriminative embeddings of trajectories from each camera to achieve robust distance measures for vehicle ReID. Moreover, we train a metadata classifier for MTMCT to obtain the metadata feature, which is concatenated with the temporal attention based embeddings. Finally, the TCLM and hierarchical clustering are jointly applied for global ID assignment. The proposed method is evaluated on the CityFlow dataset, achieving IDF1 76.77%, which outperforms the state-of-the-art MTMCT methods.
- KSP Keywords
- Attention model, Multi-Target, Re-Identification, Robust distance, Spatial and temporal, Traffic-Aware, Video sequences, camera link model, distance measure, hierarchical Clustering, multi-camera tracking