A fast Human detection algorithm for container reach stackers

Abstract

With the development of automation in the ports, more and more attention has been paid to the effective real-time monitoring of personnel intrusion into the unmanned area in the process of operation. In this paper, a fast human detection algorithm is proposed. Firstly, by improving HOG algorithm flow, the speed of feature extraction of HOG is greatly accelerated. Then, based on the HOG features, a 2-stage classifier based on Adaboost is proposed, which trains the front/back and side of the human sample library, so that the algorithm can adapt to the multi-pose human shape detection under the complex ports backgrounds. Finally, this paper presented a group of experiments about human detection on the container reach stacker of Shanghai International Port(Group) YIDONG Container Terminal Branch. The results show that the recognition accuracy of the combined algorithm of fast HOG and 2-stage classifier can reach 95% , and the detection time can be within 150ms. It realizes the calculation of 5-channel video human detection at the same time in a small embedded board, and meets the security requirements of unmanned areas in the ports.