Video Based Person Re-Identification by Re-Ranking Attentive Temporal Information in Deep Recurrent Convolutional Networks

Abstract

Person Re-identification (Person re-id) is a crucial task as its application in visual surveillance and human-computer interaction is increasing day-by-day. In this work, we present a deep learning approach for video based person re-id problem. We use residual network (ResNet) along with LSTM for feature extraction. The extracted feature is passed through an attentive temporal pooling layer, which enables the feature extractor to be aware of the current input video sequences. In this way, inter dependency between two images can directly influence the computation of each other's feature representation. At last, we re-rank the result using k-reciprocal encoding method to mitigate the effect of false matching. Experiments conducted on iLIDS-VID and PRID 2011 datasets confirm that our model outperforms existing state-of-the-art video-based re-id methods.