Object Recognition is of fundamentally importance to an intelligent visual surveillance system, and has received great attentions from both academic and industry communities. The final goal of the system is to help computer work as human being, to recognize object of interests. To achieve the goal, typical computer vision algorithm implementation has two stages: training stage and testing stage. During training stage, given the representation of object in image, we employ learned classifer to separate the data using learned hyper plane. During the testing stage, given the position of samples in feature space and the learned hyper plane in feature space, the system need to predict the label of given sample. In that case, the performance of typical computer vision algorithm depends on the discriminative ability of feature representation, and the discriminative ability of learned classifer. As increase in the data obtained by visual surveillance system, it is not practical to learn a complex classifer like nonlinear classifer. To learn the robust and discriminative feature representation is the key to address the large scale image understanding problem. Under the background of intelligent visual surveillance, we proposed to address the issue of learning mid-level feature representation for different tasks in intelligent visual surveillance system including object recognition, multi-view gait recognition, multiple camera tracking and multiple biometric fusion based pedestrian recognition.
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