7 May 2007 Human motion tracking using mean shift clustering and discrete cosine transform
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Human motion tracking is an active area of research in computer vision and machine intelligence. It has many applications in video surveillance and human-computer interface. Most of the existing algorithms track multiple humans in a given image. This paper proposes a detection approach which can track a specific person from a crowded environment. Mean shift clustering algorithm is employed in the difference image to get the candidate cluster which is found to converge within few iterations. The number of clusters and the cluster centers are automatically derived by mode seeking with the mean shift procedure. Discrete cosine transform is applied to each cluster and to the known target to extract features of the clusters and the target. To get the target cluster from a given image, Mahalanobis distance is measured between each transformed candidate cluster and the target. The cluster with the minimum distance is taken as the desired target. Tracking is carried out by updating the cluster parameters over time using the mean shift procedure.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. M. Islam, M. M. Islam, M. S. Alam, M. S. Alam, "Human motion tracking using mean shift clustering and discrete cosine transform", Proc. SPIE 6566, Automatic Target Recognition XVII, 656616 (7 May 2007); doi: 10.1117/12.717921; https://doi.org/10.1117/12.717921


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