Abstract:  Due to the influence of changes in lighting conditions and pedestrian height differences, there are large cross modal differences in surveillance video images at different times. In order to accurately identify pedestrians in cross modal images, a pedestrian recognition algorithm based on generalized transfer depth learning is proposed. The cross modal image is formed through Cyele GAN(Cycle Generative Adversarial Network), and the reference map is segmented using single object image processing to obtain candidate human body regions. The matching regions are searched in the matching map to obtain the disparity of human body regions, and the depth and perspective features of human body regions are extracted through the disparity. The attention mechanism and cross modal pedestrian recognition are combined to analyze the differences between the two types of images. The two subspaces are mapped to the same feature space. And the generalized migration depth learning algorithm is introduced to learn the loss function measurement, automatically screen the pedestrian features of the cross modal images, and finally complete pedestrian recognition through the modal fusion module to fuse the filtered features. The experimental results show that the proposed algorithm can quickly and accurately extract pedestrians from different modal images, and the recognition effect is good. 

Key words: generalization transfer deep learning')">

generalization transfer deep learning, cross-modal images, pedestrian recognition, feature extraction