| Paper ID | MMSP-5.4 |
| Paper Title |
PART-ALIGNED NETWORK WITH BACKGROUND FOR MISALIGNED PERSON SEARCH |
| Authors |
Xian Zhong, Yiting Liu, Wuhan University of Technology, China; Wenxin Huang, Hubei University, China; Xiao Wang, Wuhan University, China; Bo Ma, Independent Researcher, United States; Jingling Yuan, Wuhan University of Technology, China |
| Session | MMSP-5: Human Centric Multimedia 1 |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Multimedia Signal Processing: Multimedia Applications |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Person search is a significant computer vision task that requires addressing person detection and re-identification simultaneously. Body parts are frequently misaligned due to variation poses, occlusions, and partial missing, leading to the unsatisfied results of person search. Existing methods usually extract local features from the human body by the key point information, that cannot tackle the recognition task between a pair of persons with different body parts due to misalignment. Moreover, these methods overlook background information (e.g. the carries and the background reference object) which can also supplement effective features for representing the person. In this paper, we propose a part-aligned network with background (PANB) to address this misalignment issue. To learn local fine-grained features of different body parts, we fine-tune a parsing network to divide the body region into seven parts. In particular, our proposed method considers extracting the background features as the eighth part features to extract more robust representations, which is more rational and efficient. Furthermore, we design a reconstruction method to align the parts existing in both the query image and the cropped gallery image. Extensive experiments show that our proposed method achieves competitive performance on CUHK-SYSU and PRW datasets. |
