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Most video processing applications require object tracking as it is the base operation for real-time implementations such as surveillance, monitoring and video compression. Therefore, accurate tracking of an object under varying scene conditions is crucial for robustness. It is well known that illumination variations on the observed scene and target are an obstacle against robust object tracking causing...
In this paper we propose a computationally efficient scale adaptive tracking method using a hybrid color histogram matching scheme. Firstly, we report an important property of the Chi-squared measure- It outperforms Bhattacharyya measure in the task of histogram matching from a few significantly similar multimodal histograms. Also, Bhattacharyya measure performs better while selecting matches from...
Multiple-extremum issue including the well-known ??singularity?? problem is one of the major defects in kernel-based object tracking. This paper studies this important problem and presents a novel approach called section-based tracking (SBT) that is based on the section information provided by the division of the object's weight image. This approach serves to eliminate fake extremal points and make...
We propose a fragment based algorithm for efficient target tracking under significant scale variation and partial occlusion. In contrast, none of the previous multiple part or fragment based algorithms are both scale adaptive and robust to partial occlusion. In our algorithm, the target is divided into a number of overlapping image fragments. Their color histograms are compared with those of candidate...
This paper presents a LVQ (learning vector quantization) neural network based target differentiation method for mobile robots. The typical targets can be differentiated efficiently in indoor environments with LVQ neural network by fusing the time-of-flight data and amplitude data of sonar system. The algorithm is simple and real-time and has high accuracy and robustness. The uncertainty of sonar data...
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