Abstract
Person search is a new and challenging task proposed in recent years. It aims to jointly handle person detection and person re-identification in an end-to-end deep learning neural network. In this paper, we propose a new multi-task framework, which jointly learn person detection, person instance segmentation and person re-identification. In this framework, a segmentation branch is added into the person search pipeline to generate a high-quality segmentation mask for each person instance. Then, the segmentation feature maps are concatenated with corresponding convolution feature maps in the re-identification branch, which results as a self-attention mechanism, provides more discriminative feature for person re-identification. The experimental results on the public dataset PRW demonstrate the effectiveness of the framework.
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Acknowledgment
The research reported in this paper is supported by the Natural Science Foundation of China under Grant No. 61872047,61732017, the NSFC-Guangdong Joint Found under No. U1501254, and the National Key R&D Program of China 2017YFB1003000.
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Xue, R., Ma, H., Fu, H., Yao, W. (2019). Person Search with Joint Detection, Segmentation and Re-identification. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_52
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