The Research And Application Of Moving Object Tracking In Multi-camera Environment

Video surveillance which is used in many occasions is an important part of the security system, with its intuitive, accurate, timely and rich information content. Intelligent video monitoring is for intelligent of traditional monitoring system, so as to reduce the pressure of the personnel and expand the scope of monitoring. Moving object tracking is one of the major technical of intelligent video surveillance. As single-camera tracking system has a limited field of vision and his tracking objects are vulnerable to the interference of other obstacles, the research of moving object tracking in multi-camera environment has very important significance.This paper based on the application of moving object tracking in substation scenarios has studied the object tracking method of overlapping multi-camera, and mainly includes the following contents:1. Improved a multi-camera calibration algorithm based on one-dimensional calibration pattern. The algorithm is based on the known point correspondences to get the fundamental matrix of each set of cameras, and then utilizes each fundamental matrix to obtain projection matrices of corresponding set of the cameras. To get Euclidean matrices from projection matrices and complete multi-camera calibration, the following two phases to obtain transformation matrices which make projection matrices transform into Euclidean matrices could perform: the first phase is to solve the transformation matrices of the first set of cameras, and the second phase is using different methods to obtain the transformation matrices of other set of cameras.2. To provide input data for moving object tracking algorithm, the second chapter describes two aspects. Firstly, compared four algorithms of foreground object extraction based on background including Background Subtraction Method, One Gaussian Model, Gaussian Mixture Model and Kernel Density Estimation, and selected Background Subtraction Method as moving object extraction algorithm after the comprehensive consideration. Secondly, described in detail the construction method of probabilistic occupancy map which using foreground binary image of moving object as input data, producing the approximation of probability of the object in the scene. The approximation can be used as input data of the following moving object tracking algorithm.3. Proposed a method of k-shortest paths combining linear programming algorithm based on color information to solve multi-objective trajectory. Firstly, established a network flow based on monitoring area of multi-camera, and computed the network nodes that each time the object is most likely to pass, based on the posterior probability of object in the monitoring area. Secondly, to speed up the computation, proposed a method which uses k-shortest paths algorithm acting on network flow to get k object moving trajectory. However, k-shortest paths algorithm has not consider the color information of objects. So proposed a method of k-shortest paths combining linear programming algorithm based on color information to reduce the computational time complexity and also use the color information of object to improve the tracking accuracy.4. Designed a tracking system in substation scenarios. The system uses multi-camera to achieve real-time monitoring in indoor and outdoor areas of substation, and utilizes multi-camera tracking algorithm proposed in this paper to realize real-time tracking moving objects in monitoring area, at the same time, preserves the monitoring data to help managers.