We present a new method for object tracking; we use an efficient local search scheme based on the Kalman filter and the probability product kernel (KFPPK) to find the image region with a histogram most similar to the histogram of the tracked target. Experimental results verify the effectiveness of this proposed system.
In this paper, we explore a new correlation technique for cross-spectral image registration. The proposed technique matches the orientation feature of the second derivatives while making use of a statistical robust M estimator. Furthermore, it takes advantage of Fourier and multi-resolution techniques to reduce the complexity of spatial correlation. Simulation results show that our proposed approach gives more accurate results than the mutual information, and the normalized cross-correlation with prefiltering in terms of speed and accuracy.
Moving object tracking is a tricky job in computer vision problems. In this approach, the object tracking system relies on the deterministic search of target, whose color content matches a reference histogram model. A simple RGB histogrambased color model is used to develop our observation system. Secondly and finally, we describe a new approach for moving object tracking with particle filter by shape information. Particle filtering has been proven very successful for non-gaussian and non-linear estimation problems. In this approach we combine between particle filter and the probability product kernels as a similarity measure using integral image to compute the histograms of all possible target regions of object tracking in video sequence. The shape similarity between a target and estimated regions in the video sequence is measured by their normalized histogram. Target of object tracking is created instantly by selecting an object from the video sequence by a rectangle. Experimental results have been presented to show the effectiveness of our proposed system.
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