Visual Attention-based Moving Target Tracking System

Video-based analysis of moving objects is a core issue in the field of computer vision and there are broad prospects in the military, video surveillance, and many other areas. In this paper, I select ITS as my research example, and focused on the problems of object detection, single moving object tracking and tracking focus tracking shift, and implement and improve it.This paper is about three topics which are objects detections, single object tracking and tracking focus shift.In the part of extraction in a prominent area, I proposed a algorithm of visual attention-based objection detection. This paper introduces the concept of attention and detects the moving objects which integrated the moving feature and visual feature in the image. For the former this paper improves the detection method as follows: extract the foreground by Gaussian Mixture Model, fill them in the best gauge of the image, and then update the local background which solves the problem of image holes effectively. For the latter, the main job is to control the areas of the detected objects.In the part of the retention of tracking focus, I proposed a algorithm of the definition of the tracking region and tracking focus. The tracking was defined when system is initialed, keep the value of the prominent area, inhibit other significant area and track the moving objects in this place continuously. In the tracking process, this paper proposes a new algorithm based on multi-likelihood condensation-conditional density propagation. This algorithm can reduce the dimension of non-rigid state vector object such as rotation and the degree of tilt by verifying likelihood of the parameters in each particle (the level and vertical of coordinates) each. The experimental data shows that this algorithm plays a good job.In the part of tracking focus shift, I proposed a algorithm of the tracking shift based on the visual attention shift mechanism. All the values of prominent area were computed and sorted by the value. Finally our tracking focus shift mechanism would always concentrate the tracking focus whose value is the biggest.