The object tracking is one of the important tasks in computer vision. In this work, an object tracking method is proposed, which use the properties of the HSV colour space for representing the object. The pixels in the objects are transformed either as true colour pixels or grey colour pixels and clusters are formed either as a true colour cluster or as a gray colour cluster. The adaptive K-means clustering is applied for clustering the video frames and a suitable post processing is carried out for obtaining information about all clusters. A suitable similarity measure has been proposed by which the object level similarity between the current frame and next frame are calculated. The performance of the proposed approach is evaluated using bench mark video sequences such as PETS, SPEVI and also some proprietary videos. The performance of proposed approach is found to be encouraging compared to recently proposed approaches.
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