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Camshift is an efficient object tracking algorithm using color probability distribution. But it's easy to be interfered by the similar color objects in the background. So we bring in the motion characteristics extracted by the motion template method, and then make an improved Camshift algorithm. Experiments show that the improved algorithm has more robustness in dealing with color interference and...
In this paper, we propose a new algorithm for optimally adapting ellipses outlining objects of interest in order to improve the performance of a colour based tracking approach for real video sequences. We present a Lagrangian based method to integrate a regularising component into the covariance matrix to be computed. Technically, we intend to reduce the residuals between the estimated probability...
This paper presents methods for vision-based detection and tracking of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. The goal of this research is to develop suitable methods for automatic visual traffic surveillance to perform detection, tracking and traffic parameter estimation of multiple vehicles in real time as well as tackle environment illumination...
To realize the real-time tracking of the shoot point from light pen, we choose Camshift tracking algorithm, but it is difficult to track the point with dynamic and complicated background and in case that there is similar color with the shoot point from light pen. So, an improved method is proposed. Firstly, a red filter which weakens the effect of background, and makes the object prominent is introduced...
Vision based target tracking under complicated background is difficult for image analysis. Cam-shift (continuously adaptive mean-shift) algorithm which is based on color cue can track objects with certain color in a simple scene. But under complicated background, tracking converges to another local maximum and loses the real target because of the varying illumination, occlusion, etc. To deal with...
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