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This paper presents an improved Mean Shift tracking algorithm. It extends the classic Mean Shift tracking algorithm by combining color and texture features. In the proposed method, firstly, both the color feature and the texture feature of the target are extracted from first frame and the histogram of each feature is computed. Then the Mean Shift algorithm is run for maximizing the similarity measure...
A new scheme is put forward to realize robust and real-time object tracking by CamShift combining color information and improved LBP. Continuous adaptive mean shift (CamShift) algorithm is a good choice for object tracking with its high speed and insensitiveness to the rotation and size of the target, but it is influenced by environmental lights and color information. Local binary patterns (LBP) is...
In this paper we present a new method combining mean shift with Kalman filter for human tracking. Firstly, we use the mean shift algorithm based on color and texture features to calculate an accurate location in current frame. We select the HSV color space for calculating the histogram. Then Kalman filter is applied to predict the next initial searching location for mean shift iterations in the next...
Object tracking is often disturbed by visual occlusion. To handle this problem, we have previously proposed the tracking method by the particle filter, which switches tracking targets autonomously. This method enables the tracker to track the occluded target indirectly by switching its target to the occluder effectively. However, the color-based target model used in this method often causes inaccurate...
A new algorithm for face tracking is proposed. Color information provides an effective cue for face tracking due to its robustness to scaling, rotation and translation. But the main limitation of color cue is that it can be easily interrupted by the camouflage objects that have the same or similar color with the target face. In order to achieve robust face tracking performance, the texture feature...
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