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In this paper, we present a texture-based multitarget tracking algorithm. Moving objects are described by local binary patterns (LBP), which is a kind of discriminative texture descriptor. The Kalman filter is introduced into the algorithm to predict the blob's new position and size. Blobs are searched in the neighborhood of the Kalman predictions. If more than one are found, the LBP distance, which...
In this paper, an integrated video surveillance system for robust tracking is introduced. In the blob detection part, an optical flow algorithm for crowded environment is studied experimentally and a comparison study with respect to traditional subtraction approach is carried out. In the segmentation part, different algorithms are fused to develop a hybrid algorithm for stable segmentation, and validation...
A robust approach to detection and tracking of multiple moving targets from a moving camera is presented. The main novelty of this approach is that objects are represented using efficient compact form of the colour correlogram. Like previous correlograms, this encodes both spatial pattern and appearance information about the target. However it is less complex to compute, making it applicable to real...
In this paper we present a novel framework for learning contextual motion model involving multiple objects in far-field surveillance video and apply the learned model to improving the performance of objects tracking and abnormal event detection. We represent trajectory of multiple objects by a 3D graph G in x,y,t, which is augmented by a number of spatio-temporal relations (links) between moving and...
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