Person search is to detect all persons and identify the query persons from detected persons in the image without proposals and bounding boxes, which is different from person re-identification. In this paper, we propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity of detection and re-identification with a single convolutional neural network. We focus on how the interplay of person detection and person re-identification affects the overall performance. We employ person labels in region proposal network to produce features for person reidentification and person detection network, which can improve the accuracy of detection and re-identification simultaneously. We also use a multiple loss to train our re-identification network. Experiment results on CUHK-SYSU Person Search dataset show that the performance of our proposed method is superior to state-of-the-art approaches in both mAP and top-1.Keywords Person search · heterogeneous task · multiple loss · region proposal network · person labels 1 IntroductionPerson search combines person detection and person re-identification, which detects all candidate persons in an image and then compares all possible pairs of the query persons to identify the target persons, which is different from person re-identification. It is a challenging and fast-growing field. It has many important applications in video surveillance and multimedia, such as pedestrian retrieval[1] and cross-camera visual tracking [2]. The recent work [3] proposed an end-to-end person search model based on a single convolutional neural network, which adopts proposed Online Instance Matching (OIM) loss function to

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