Improving Person Search by Adaptive Feature Pyramidbased MultiScale Matching Within person search problem, existing pedestrian detectors usually generate person bounding boxes with different sizes, which will degrade the performance of person reidentification in matching process. The builtin feature pyramid hierarchy to address the multiscale matching problem adopted in previous studies, is prone to be effective for reducing the semantic gaps among different level features. Therefore, in this paper we propose an Adaptive Feature Pyramid (AFP)based multiscale matching method for person search. The network adaptively learns a set of adjusting vectors to make the lowlevel feature distributions approximate the highlevel. Moreover, we integrate the AFP structure into a ResNet50 based person search framework to search a target person from a gallery of whole scene images. Extensive experimental results show that our approach performs superiority over the stateoftheart person search methods on CUHKSYSU dataset.

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.