Multi‐stage attention network for video‐based person re‐identification

Abstract

Video-based person re-identification (Re-ID) has received increasing attention in video surveillance analysis in recent years. To extract relevant information of the target, many existing methods utilise the attention mechanism in the residual block of the ResNet. However, these methods only focus on the residual block and ignore the output of the shortcut part, which also contains rich information about the person. To solve this problem, a different aspect of network design is investigated: the insert position of the attention module. To simultaneously explore the discriminative information in both the residual block and the shortcut, a novel multi-stage attention method is proposed by inserting the attention mechanism between stages of ResNet. Using this method can effectively extract the rich discriminative features of the target to better distinguish different pedestrians and improve the feature extraction capabilities of the model. Extensive experiments are conducted on four popular video-based person Re-ID datasets to demonstrate the effectiveness of the authors’ proposed method and display its superiority with the existing video-based person Re-ID methods.