FMT:Fusing Multi-task Convolutional Neural Network for Person Search
This work addresses the challenge of person search in computer vision, which is important for applications like surveillance, but it is incremental as it builds on existing multi-task learning approaches.
The paper tackles the problem of person search by proposing a fusing multi-task convolutional neural network (FMT-CNN) that integrates detection and re-identification into a single model, resulting in superior performance on the CUHK-SYSU dataset with improved mAP and top-1 accuracy compared to state-of-the-art methods.
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 re-identification 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.