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An automatic object tracking technology is developed for a digital camera. We have developed a new modified object tracking algorithm based on K-means tracker algorithm that is adaptive to the change of the object shape and appearance. Several modifications for higher tracking performance and lower computation cost are introduced for the digital camera application.
The paper presents an efficient and reliable approach to automatic people segmentation, tracking and counting, designed for a system with an overhead mounted (zenithal) camera. Upon the initial block-wise background subtraction, k-means clustering is used to enable the segmentation of single persons in the scene. The number of people in the scene is estimated as the maximal number of clusters with...
This paper describes a system for automatically extracting meta-information on people from videos on the Web. The system contains multiple modules which automatically track people, including both faces and bodies, and clusters the people into distinct groups. We present new technology and significantly modify existing algorithms for body-detection, shot-detection and grouping, tracking, and track-clustering...
Persistence of objects in scenes is an important parameter of video object tracking systems. From the analysis of objects' durations (of stay) we not only get how long they stay in the scene, but also precisely where the objects spend time. The video frame is therefore segmented into clusters, and objects which go through or stay there are assigned to that cluster. If we observe all objects in a time...
We describe a method for tracking people using a time-of-flight camera and apply the method for persistent authentication in a smart-environment. A background model is built by fusing information from intensity and depth images. While a geometric constraint is employed to improve pixel cluster coherence and reducing the influence of noise, the EM algorithm (expectation maximization) is used for tracking...
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