A robust target recognition and tracking panoramic surveillance system based on deep learning

Abstract

A panoramic monitoring system is designed to achieve continuous monitoring of the surrounding environment. The image acquisition module of the system is composed of five fixed-focal-length cameras and a variable-focal-length camera, which realizes 360 degree environmental monitoring. Usually, the background of continuous photography changes due to fluctuations of ambient light, humidity and wind. Therefore, a dynamic adaptive threshold is used to dynamically update the background template in order to better accommodate various weather changes. Further, a motion-aware algorithm based on background updates is applied to effectively detect whether an intruding target exists and determine the direction of the target. Once an intrusive target is found, the deep convolution neural network Yolo is employed to recognize the target quickly. It shows the advantages of less computation and preferable detection accuracy. In addition, according to the preset warning level, when the intrusion target needs to be alarmed, the target orientation is transmitted to the platform through the central control processing unit, so that the variable-focal-length camera can take real-time snapshots. we propose an end-to-end lightweight siamese convolution neural network to achieve fast and robust target tracking. The network structure replaces the hand-crafted features by the multi-layers deep convolution features of the target, so that higher precision can be achieved. The experiment result shows panoramic surveillance system can effectively and robustly perform security tasks such as panoramic imaging, target recognition and fast target tracking.