# Rethink Motion Information for Occluded Person Re-Identification
###### Abstract  
Person re-identification aims to identify the same pedestrians captured by various cameras from different viewpoints in multiple scenarios. Occlusion is the toughest problem for practical applications. In video-based ReID tasks, motion information can be easily obtained from sampled frames, and provide discriminative human part representations. However, most motion-based methodologies are designed for video frames which are not suitable for processing single static image input. In this paper, we propose a Motion-Aware Fusion (MAF) network, aiming to acquire motion information from static images in order to improve the performance of ReID tasks. Specifically, a visual adapter is introduced to enable visual feature extraction, either from image or video data. We design a motion consistency task to guide the motion-aware transformer to learn representative human-part motion information and greatly improve the learning quality of features of occluded pedestrians. Extensive experiments on popular holistic, occluded, and video datasets demonstrate the effectiveness of our proposed method. This method outperforms state-of-the-art approaches by improving the mean average precision (mAP) by 1.5% and rank-1 accuracy by 1.2% on the challenging Occluded-REID dataset. At the same time, it surpasses other methods on the MARS dataset with an improvement of 0.2% in mAP and 0.1% in rank-1 accuracy. person re-identification; motion-aware; occlusion +
Footnote †: journal: Medical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China; liuhongye1998@163. Compared to the general person re-identification (ReID) problem, the current challenge of occluded person ReID is two-fold. Firstly, interference from unknown objects can cause significant fluctuations in human features, leading to difficulties in feature extraction. To address this issue, previous methods [8; 15] have employed targeted occlusion data enhancement or introduced a more robust pre-trained model. However, these approaches tend to focus more on the person's appearance features and memorize specific occlusion types, resulting in a lack of robustness in the extracted human-part features. In real-world scenarios, occlusion types are often unpredictable and randomly located, making specific occlusion data augmentation limited in terms of its generalizability across the entire domain. Secondly, exploring more representative person features is crucial for the occluded person ReID framework. Multi-pedestrian occlusions [16; 17] are particularly challenging compared to other types of occlusions. In these scenarios, the model's ability to distinguish the features of different pedestrians becomes even more important [18]. However, relying solely on the external features of a pedestrian's appearance is not sufficient. Implicit features are also needed for effective ReID. To address these challenges, we propose three frameworks that deeply examine the implicit motion information and explicit visual characteristics of pedestrians. The first framework focuses on the dynamic processing of image or video feature inputs, providing a unified architecture for both image-based and video-based ReID tasks. We introduce a visual adapter with a set of learnable visual queries to integrate visual features from the visual encoder and reduce computational complexity in the cross-attention between motion and visual information. The adapter considers a single image as a single-frame video, enabling our framework to handle both image-based and video-based ReID tasks. The second framework seeks to obtain implicit motion information through a motion-aware transformer. By passing the integrated visual features through the transformer, we establish another learnable query to obtain human per segment motion representations.