Abstract
In recent years, the Mean shift algorithm has extensive applications in the field of video tracking. It has some advantages of low cost, small memory, and good tracking effect. However, there are some shortcomings in the existing algorithm; for example, it cannot produce adaptive changes as the target size changes. And when there are similar objects, it is prone to target positioning errors and tracking failures caused by occlusion. In this paper, an improved method of continuous adaptive change Mean shift (Camshift) for high-precision positioning and tracking is proposed. The traditional Camshift method only uses hue components in HSV to extract features. This paper uses the combination of H and S components in HSV space to build a two-dimensional color feature histogram and with the image’s LBP feature histogram to increase tracking accuracy. Meanwhile, for the sake of target occlusion and nonlinear changes in the tracking process, this paper introduces a Gaussian-Hermit particle filter that is updated by the Kalman filter. Experimental result demonstrates that the real-time performance of the proposal in this paper is better than Mean shift, Camshift, simple particle filter, and Kalman filter.
Highlights
With the wider application of video tracking, scholars continue to propose various methods [1, 2]
In order to cope with the tracking environment where similar object interference appears, [12] uses the H and V vectors on HSV to construct the histogram to enhance the features of the target so that the tracking accuracy is increased
We propose to take advantages of the weighted feature histograms of H and S in HSV to describe the characteristics of the target and use the inverse mapping distribution of the color histogram to improve the tracking accuracy
Summary
With the wider application of video tracking, scholars continue to propose various methods [1, 2]. The Kalman algorithm [15] is treated as predictor to forecast the target globally and to obtain more accurate target coordinates This improvement can reduce the error of the tracking when the object moves too fast. As [25] performs arithmetic operations on each particle set, this leads to a large amount of calculation and makes the particle set too concentrated in this method At this time, when the tracked object is partially or completely occluded and reappears, which will cause object tracking failure. The Gaussian-Hermitian particle filter proposed in [30] is intended to generate the importance density function of the particle filter, and the Kalman filter is used for global motion estimation to obtain better state estimation accuracy.
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