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Background subtraction has always been an important field in computer vision since it is generally the first step in video or image processing. It is an even more challenging job when sequences are taken by moving cameras. Unlike static surveillance cameras, the background varies a lot in every frame taken from mobile cameras. In this sense an optical flow based clustering method is proposed. The...
We propose a novel method to represent motion trajectory - 'bag of segments'. Motivated by the 'bag of words' approach in text mining and more recently in image categorization, we represent each trajectory as a collection of segments, each assigned a membership to a codeword of a dictionary. The trajectory segments are represented by their shape and motion information while the inter-segment relationship...
Trajectories of moving objects provide crucial clues for video event analysis especially in surveillance applications. In this paper, we study the problem of detecting anomalous events by analyzing the motion trajectories in videos. Different trajectories of the same category may have varying relative velocities, in addition to the variations and noises in location samples; hence the core of the problem...
Building face models is an essential task in face recognition, tracking and etc. However, most of the current techniques require hand-labelling or special machinery such as cyber-scanner to extract the face model. In the paper, we propose an unsupervised algorithm to learn the face texture from video. The proposed approach models the video sequence as a mixture of dynamic face-layers and background...
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