Sensors, Vol. 19, Pages 3861: Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification

Sensors, Vol. 19, Pages 3861: Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification Sensors doi: 10.3390/s19183861 Authors: Changxin Gao Jin Wang Leyuan Liu Jin-Gang Yu Nong Sang Most existing person re-identification methods focus on matching still person images across non-overlapping camera views. Despite their excellent performance in some circumstances, these methods still suffer from occlusion and the changes of pose, viewpoint or lighting. Video-based re-id is a natural way to overcome these problems, by exploiting space–time information from videos. One of the most challenging problems in video-based person re-identification is temporal alignment, in addition to spatial alignment. To address the problem, we propose an effective superpixel-based temporally aligned representation for video-based person re-identification, which represents a video sequence only using one walking cycle. Particularly, we first build a candidate set of walking cycles by extracting motion information at superpixel level, which is more robust than that at the pixel level. Then, from the candidate set, we propose an effective criterion to select the walking cycle most matching the intrinsic periodicity property of walking persons. Finally, we propose a temporally aligned pooling scheme to describe the video data in the selected walking cycle. In addition, to characterize the individual still images in the cycle, we propose a sup...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research
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