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| Content Provider | ACM Digital Library |
|---|---|
| Author | Chen, Jun Liang, Chao Wang, Yimin Fang, Wenhua Hu, Ruimin |
| Abstract | Vehicle re-identification, retrieving a vehicle detected by one camera with the same vehicle by another camera, is an important problem in the video investigation application which is a technology for criminal investigation. In this task, it not only needs to classify the vehicle category, but also to identify a specific object in the category. Previous methods mainly focus on the vehicle categorization, which cannot identify the specific vehicle. In this paper, a two-stage strategy is proposed to accomplish vehicle re-identification in realistic surveillance videos. Specifically, in the first stage, a part-based appearance model fusing multiple visual features is proposed to represent the vehicle object, and then a coarse ranking list is generated by comparing appearance models of the probe and gallery vehicles. In the second stage, the temporal-spatial relation is introduced to re-rank the above visual-based ranking list, where vehicles of the same category and reasonable spatial-temporal relations are placed in top positions while those of mismatched types or relations are placed in rear positions. Both quantitative and qualitative experiments conducted on a real world dataset have validated the effectiveness of the proposed method. |
| Starting Page | 121 |
| Ending Page | 125 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450322522 |
| DOI | 10.1145/2499788.2499844 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-08-17 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Temporal-spatial network Vehicle re-identification |
| Content Type | Text |
| Resource Type | Article |
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