Person Search with Joint Detection, Segmentation and Reidentification. Person search is a new and challenging task proposed in recent years. It aims to jointly handle person detection and person reidentification in an endtoend deep learning neural network. In this paper, we propose a new multitask framework, which jointly learn person detection, person instance segmentation and person reidentification. In this framework, a segmentation branch is added into the person search pipeline to generate a highquality segmentation mask for each person instance. Then, the segmentation feature maps are concatenated with corresponding convolution feature maps in the reidentification branch, which results as a selfattention mechanism, provides more discriminative feature for person reidentification. The experimental results on the public dataset PRW demonstrate the effectiveness of the framework.

Improving Person ReIdentification Performance Using Body Mask Via CrossLearning Strategy The task of person reidentification (reid) is to find the same pedestrian across nonoverlapping cameras. Normally, the performance of person reid can be affected by background clutters. However, existing segmentation algorithms are hard to obtain perfect foreground person images. To effectively leverage the body (foreground) cue, and in the meantime pay attention to discriminative information in the background (e.g., companion or vehicle), we propose to use a crosslearning strategy to take both foreground and other discriminative information into account. In addition, since currently existing foreground segmentation result always involves noise, we use Label Smoothing Regularization (LSR) to strengthen the generalization capability during our learning process. In experiments, we pick up two stateoftheart person reid methods to verify the effectiveness of our proposed crosslearning strategy. Our experiments are carried out on two publicly available person reid datasets. Obvious performance improvements can be observed on both datasets.