Cross-modality collaborative learning identified pedestrian

Cross-modal pedestrian re-identification is a key technology to realize all-weather intelligent video surveillance system. This technology is designed to match the visible light image and infrared image of a pedestrian with a specific identity in a non-overlapping camera scene, so it faces huge intra-class changes and modal differences. Existing methods are difficult to solve these two difficulties, which is largely due to the lack of effective mining of feature discrimination and the full use of multi-source heterogeneous information. In view of the above shortcomings, a refined multi-source feature collaborative network is designed by the collaborative learning method, and multiple complementary features are extracted for information fusion, the learning ability of the network is improved. Multi-scale and multi-level features are extracted from the backbone convolutional network, and the refined feature collaborative learning is realized; the discriminative ability of features is enhanced to deal with intra-class changes. A modal sharing and unique feature collaboration module and a cross-modal human semantic self-supervision module are designed to achieve the purpose of multi-source feature collaborative learning, so as to improve the utilization of multi-source heterogeneous image information, and then modal differences are resolved. The validity and advancement of this method are verified on the SYSU-MM01 and RegDB data sets.