Abstract
Human detection is an important research branch in the field of computer vision, and it is widely used in vehicle-assisted driving, intelligent monitoring, intelligent transportation and other aspects. In order to solve the problems of poor real-time performance, low detection accuracy and inability to detect under occlusion in traditional human detection methods. This paper proposes a deep learning based trainable cross-view human detection system, where the encoder-decoder network focuses on solving the feature association problem after perspective transformation. Then, the system was tested on the WildTrack dataset. The results show that our cross-view human detection system outperforms conventional systems in terms of speed and accuracy across the board, achieving a satisfactory performance rate of 0.71 MODA in the presence of extensive occlusion.
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