Aggregate Channel Features Based on Local Phase, Color, Texture, and Gradient Features for People Localization

Detection of human beings in a complex background environment is a great challenge in computer vision. For such a difficult task, most of the time no single feature algorithm is rich enough to capture all the relevant information available in the image. To improve the detection accuracy, we propose a new descriptor that is constructed from the channels of image gradients, texture features, local phase information, and color features. This information is fused together to build one descriptor named Channels of Chromatic domain, local Phase with Gradient and Texture features (CCPGT). The image gradients, and local phase information based on the phase congruency concept are used to extract the human body shape features. Local binary pattern approach (LBP) is used to capture the texture features, and additional significant information for human detection are added by the color channels. The phase congruency magnitude and orientation of each pixel in the input image is computed with respect to its neighborhood and then six channels representing the histogram of oriented phase are generated. Gradient magnitude channel, LBP channel, and three LUV color channels are also computed for the input image. A maximum pooling of the candidate features is applied for these generated channels. All these features are concatenated in one feature vector and fed to a decision tree Adaboost classifier for training and to classify between the different objects classes. Several experiments were conducted to evaluate the proposed approach using challenging INRIA dataset, and a promising performance is observed.