Temporally aligned pooling representation for video-based person re-identification

Changxin Gao, Jin Wang, Leyuan Liu, Jin-Gang Yu, Nong Sang

Abstract: This paper proposes an effective Temporally Aligned Pooling Representation (TAPR) for video-based person re-identification. To extract the motion information from a sequence, we propose to track the superpixels of the lowest portions of human. To perform temporal alignment of videos, we propose to select the “best” walking cycle from the noisy motion information according to the intrinsic periodicity property of walking persons, that is fitted sinusoid in our implementation. To describe the video data in the selected walking cycle, we first divide the cycle into several segments according to the sinusoid, and then describe each segment by temporally aligned pooling. Extensive experimental results on the public datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches.