Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (20): 152-157.DOI: 10.3778/j.issn.1002-8331.1908-0100

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Research on Impact of Different Temporal Modeling Methods on Video-Based Person Re-identification

XIANG Jun, LIN Ranran, HUANG Ziyuan, HOU Jianhua   

  1. College of Electronics and Information Engineering, South-Central University for Nationalities, Wuhan 430074, China
  • Online:2020-10-15 Published:2020-10-13



  1. 中南民族大学 电子信息工程学院,武汉 430074


As an important task in computer vision field, video-based person re-identification(Re-ID) has attracted significant attention in recent years due to the increasing demand of video surveillance. Typically, a video-based person Re-ID system is composed of an image-level feature extractor(e.g.CNN), a temporal modeling method to capture temporal information and a loss function. In this paper, it explores the impact of different temporal modeling methods on video-based person Re-ID by fixing the image-level feature extractor and the loss function to be the same. Three temporal models are considered, including temporal pooling, temporal attention and Recurrent Neural Network(RNN). Experimental results conducted on the MARS dataset demonstrate that compared with the image-based Re-ID baseline, both temporal pooling and temporal attention models can improve the recognition accuracy, and RNN’s performance has dropped to some extent. The conclusions achieved in the paper can provide helpful insight for the design of video-based person re-identification algorithms.

Key words: video-based person re-identification, deep neural networks, feature extracting, temporal model



关键词: 视频行人重识别, 深度神经网络, 特征提取, 时域模型