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A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented in this paper. The proposed framework essentially turns the anomaly detection process into two parts, namely, motion pattern representation and crowded context modeling. During the first stage, we averagely divide the spatiotemporal volume into atomic blocks. Considering...
Multi-scale parts-based models are particularly effective for recognizing non-segmented graphic symbols, i.e. the symbols interfered by other connecting or intersecting objects in the context. However, treating every symbol part and it features on every scale equally, despite some of them contributing little to the recognition, may lead to unnecessarily high dimensional representation of the symbol...
A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented. The proposed framework essentially turns the complex anomaly detection process into two parts: motion pattern representation and spatio-temporal context modeling. We propose a new 4D spatio-temporal hypervolume representation by integrating the depth constraints to enrich...
We present an ensemble recognition method for graphic symbols that could be interfered by intersecting objects from the context. The symbol is first represented as a set of shape points, each of which is described by a shape context pyramid capturing the local shape characteristics of multi-scale regions surrounding the shape point. A Hough forest ensemble classifier is then employed to learn the...
We present a multi resolution scheme for symbol representation and recognition based on statistical shape features. We define a symbol as a set of shape points, each of which is then described by a pyramid of shape context features. The pyramid is constructed by successively partitioning the image surrounding one shape point into increasingly finer sub-regions and computing the local shape context...
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