Background tracking of a video taken from a front camera of non maneuvering vehicle

Ünver, Önder
In this study, a novel background tracking technique is proposed that uses extended Kalman Gaussian mixture probability hypothesis density filtering approach. Since the background in a movie, taken from a front camera of a non maneuvering moving vehicle, exhibits a non-stationary nature, tracking the background is usually done by using pixel-wise comparisons in consequent frames. Besides, some methods use features of the background to track it. The proposed method uses the feature tracking approach. The features are chosen as the corner points extracted from each video frame by using Harris corner detector. Linear motion model and non-linear measurement model are developed to predict and update the states of the features. Based on these models, the time varying number of features are tracked by extended Kalman Gaussian mixture probability hypothesis density filter. The method propagates the intensities of the targets based on random set theory and the Kalman filtering approach. MATLAB environment is used to implement the proposed background tracking method. Some simulated results of proposed method are shown for different conditions. The results indicate that the proposed method can be used for background tracking of a video instead of classical background tracking methods under some assumptions.