TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Viewpoint Invariant Person Re-Identification for Global Multi-Object Tracking with Non-Overlapping Cameras |
| 영문제목(English Title) |
Viewpoint Invariant Person Re-Identification for Global Multi-Object Tracking with Non-Overlapping Cameras |
| 저자(Author) |
Jeonghwan Gwak
Geunpyo Park
Moongu Jeon
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| 원문수록처(Citation) |
VOL 11 NO. 04 PP. 2075 ~ 2092 (2017. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Person re-identification is to match pedestrians observed from non-overlapping camera views. It has important applications in video surveillance such as person retrieval, person tracking, and activity analysis. However, it is a very challenging problem due to illumination, pose and viewpoint variations between non-overlapping camera views. In this work, we propose a viewpoint invariant method for matching pedestrian images using orientation of pedestrian. First, the proposed method divides a pedestrian image into patches and assigns angle to a patch using the orientation of the pedestrian under the assumption that a person body has the cylindrical shape. The difference between angles are then used to compute the similarity between patches. We applied the proposed method to real-time global multi-object tracking across multiple disjoint cameras with non-overlapping field of views. Re-identification algorithm makes global trajectories by connecting local trajectories obtained by different local trackers. The effectiveness of the viewpoint invariant method for person re-identification was validated on the VIPeR dataset. In addition, we demonstrated the effectiveness of the proposed approach for the inter-camera multiple object tracking on the MCT dataset with ground truth data for local tracking.
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| 키워드(Keyword) |
Viewpoint invariance
person re-identification
global multi-object tracking
non-overlapping cameras
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| 파일첨부 |
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