Particle filter supported with the neural network for aircraft tracking based on kernel and active contour

In this paper we present a new method for aircraft tracking in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light conditions, large displacement, speed changing, and occlusion. In fact, we want to achieve an exact contour of the target in each frame of the video. The proposed method is made in three steps, estimating the location of the target by the particle filter, segmentation of the region of the target using neural networks and finding the exact contours by greedy snake algorithm. In the proposed method we have used both regions and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation error during the update step and achieving higher segmentation accuracy, the target region is given to a perceptron neural network to separate the target from the background, after estimation of the target location. The output is used for exact calculation of the size and the center of the target. Moreover, it is used as the initial contour for the greedy snake algorithm to find the exact edge of the target. The proposed algorithm has been tested on two databases which contain challenges like highspeed and agility of aircrafts, background clutter, occlusions and camera movements. The experimental results show that our method increases the accuracy of tracking and segmentation.