Pedestrian Search Method Based on Faster R-CNN with the Integration of Pedestrian Detection and Re-identification

For closing the gap between research of pedestrian re-identification and pedestrian search in real-world applications,this paper proposes a new pedestrian search method by fusing the pedestrian detection and re-identification modules based on the modified Faster R-CNN.Firstly,it used an iterative bounding box regression network to promote the precision of bounding boxes.Then to enhance similarity learning ability,it used a modified metric learning method named MSLF which consists both cosine distance and Euclidean distance.Finally it added center loss to the whole loss function of network.Center loss boosts the network’s ability by extracting discriminative features of different pedestrians,and enables the network to achieve a better result for query pedestrian search.It performed simulation on a large scale benchmark dataset named CUHK-SYSU,the experimental results show that proposed method achieves 81.6%in CMC top-1,and 78.9%in mAP,which outperforms other paralleling methods about 3.0%-18.0%in CMC top-1 and 3.0%-23.0%in mAP.