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In this work, we address the problem of reducing the false positives for human detection in videos. We employ the motion cue to build a foreground probability model. Then the mean expectation of the pixel-level foreground probability is computed to assign a priori probability to the sliding window in detection. We combine the response of Deformable Part Models and the mean probability expectation...
In this paper, we present an action recognition framework based on binary stochastic latent variables model, Hidden unit Conditional Random Fields(HuCRF). It is a chain structured undirected graphs model with nonlinear dependencies at each frame/segment, contrast to standard log-linear models like CRF. So it is more powerful in sequence modeling tasks like action recognition. The observations of actions...
Detecting motion pattern in dynamic crowd scenes is a challenging problem in computer vision field. In this paper, we propose a novel approach to detect the motion patterns from global perspective. To extract the discriminative spatial-temporal features, we introduce the Motion History Image (MHI) into the optical flow algorithm. Motion patterns are then detected by automatic clustering of optical...
Detecting text and caption from videos is important and in great demand for video retrieval, annotation, indexing, and content analysis. In this paper, we present a corner based approach to detect text and caption from videos. This approach is inspired by the observation that there exist dense and orderly presences of corner points in characters, especially in text and caption. We use several discriminative...
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