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

A panoramic surveillance 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 one variable-focal-length camera, which realizes 360 degree environmental surveillance. An adaptive threshold is used to dynamically update the background template in order to better accommodate various weather changes. Further, a pixel-level video moving target detection algorithm is applied to effectively detect whether an intruding target exists and determine the direction of the target. It shows the advantages of less computation and preferable detection accuracy. Once an intrusive target is found, the deep convolution neural network SSD is employed to recognize the specific target quickly. As common sense, visual object tracking is one of the most attractive issue in computer vision. Recently, deep neural network has been widely developed in object tracking and shown great achievement. Here, we propose an end-to-end lightweight siamese convolution neural network to achieve fast and robust target tracking. The experiment result shows panoramic surveillance system can effectively and robustly perform security tasks such as panoramic imaging, target recognition and fast target tracking. At the same time, the deep convolution neural network can recognize and track the target accurately and quickly, which meets the real-time and accuracy requirements of practical task.