Particle Filter Supported with the Neural Network for Aircraft Tracking Based on Kernel and Active Contour

In this paper we presented a new method for tracking
flying targets 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, large displacement, changing speed, and occlusion.
The proposed method is made in three steps, estimate the target
location by particle filter, segmentation target region using neural
network and find the exact contours by greedy snake algorithm. In
the proposed method we have used both region and contour
information to create target candidate model and this model is
dynamically updated during tracking. To avoid the accumulation of
errors when updating, target region given to a perceptron neural
network to separate the target from background. Then its output used
for exact calculation of size and center of the target. Also it is used as
the initial contour for the greedy snake algorithm to find the exact
target's edge. The proposed algorithm has been tested on a database
which contains a lot of challenges such as high speed and agility of
aircrafts, background clutter, occlusions, camera movement, and so
on. The experimental results show that the use of neural network
increases the accuracy of tracking and segmentation.