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In this paper, we propose a new approach for pedestrian detection in crowded scene from static images.The method is based on hybrid features, one type of middle-level features, which include Haar-like features and gradient features, two low-level feature sets. The haar-like features focus on the local edges information of the image and the gradient features focus on the local regions information....
Detecting human in still images is one of the most challenging object detection problems. In this paper we apply the scale theory to human detection. By integrating Gaussian Pyramids multi-scale object representation approach we present a Learned Multi-scale Mid-level Feature (LMMF) based human detection algorithm. Firstly multiscale low-level features are extracted by Gaussian Pyramid decomposition...
Feature selection is a key issue in many machine learning applications and the need to test lots of candidate features is real while computational time required to do so is often huge. In this paper, we introduce a parallel version of the well-known AdaBoost algorithm to speed up and size up feature selection for binary classification tasks using large training datasets and a wide range of elementary...
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