Research on person re-identification by fusing posture information and attention mechanisms
To address the problem of pedestrian occlusion and messy background information in the task of pedestrian re-identification(Re-ID),the human body key point model was adopted to locate the key point information of the pedestrians to eliminate the background information,and the image was segmented into semantic information based on the key point information.In order to make the extracted features of backbone network more robust,enhanced attention module(EAM)was designed,which allows the network to automatically assign weights,and the more recognizable feature vectors were finally obtained.These parts and the overall image were fed into a neural network that incorporates the modified attention mechanism and optimized the network by combining multiple losses.Experiments on several pedestrian re-recognition datasets validate that the proposed method outperforms most state-of-the-art methods.In addition,the experimental results also show that the network has a positive effect on the cross-domain and occlusion problems.
person re-identification(Re-ID)gesture informationattention modulechunking featurefeature fusioncross-domain recognition
NETL
NSTL
万方数据
