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Local image features in space-time or spatio-temporal interest points provide compact and abstract representations of patterns in a video sequence. In this paper, we present a novel human action recognition method based on multi-velocity spatio-temporal interest points (MVSTIPs) and a novel local descriptor called motion energy (ME) orientation histogram(More)
A novel approach to detect vehicles in night outdoor scenes is proposed in this paper. By using online subspace learning, the preliminary foreground is obtained based on spatio-temporal bricks. Meanwhile, a new operator named 3DLBP is proposed to describe the local features of bricks. The final foreground is acquired by region-grow with 3DLBP and(More)
Feature operators can transform raw pixel values of an image into a representation better suited to the later processing and classification steps in the face recognition system. In this paper, we evaluate the performance of 6 feature extraction methods, i.e., Local Binary Patterns, Histograms of Oriented Gradients, Scale Invariant Feature Transform,(More)
A novel and efficient human action recognition method utilizing spatio-temporal interest point detector and 3D speed up robust features (3D SURF) descriptor is proposed. The spatio-temporal interest points are detected using two separate linear filters. Then 3D SURF descriptor is presented and demonstrated in detail to represent the local region around(More)
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