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In this paper, we propose an integrated system for scale-variance pedestrian detection. It consists of two cascaded components: a multi-layer detection neural network (MLDNN) for scale-variance pedestrian detection, and a fast decision forest (FDF) for boosting detection performance with only a slight decrease in speed. Experimental results on the Caltech Pedestrian dataset show that our approach...
Face recognition methods utilizing Sparse Representation based Classification (SRC) and Collaborative Representation based Classification (CRC) have recently attracted a great deal of attention due to inherent simplicity and efficiency. In this paper, we introduce the Large Margin Nearest Neighbor (LMNN), which learns a Mahalanobis distance metric that is applied, to SRC and CRC as the locality constraint...
In this paper, we propose a novel foreground segmentation approach for applications using static cameras. The foreground segmentation is modeled as an energy function optimum process, where energy function is based on Markov Random Field (MRF) and efficiently optimized by Gibbs sampling. The essence of our method is that we fuse four foreground/background models based on color and texture. This allows...
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