Human Detection in Infrared Imagery using Intensity Distribution, Gradient, and Texture Features

摘要

Many human detection algorithms are able to detect humans in various environmental conditions with high accuracy, but they strongly use color information for detection, which is not robust to lighting changes and varying colors. This problem is further amplified with infrared imagery, which only contains gray scale information. The proposed algorithm for human detection uses intensity distribution, gradient and texture features for effective detection of humans in infrared imagery. For the detection of intensity, histogram information is obtained in the grayscale channel. For extracting gradients, we utilize Histogram of Oriented Gradients for better information in the various lighting scenarios. For extraction texture information, center-symmetric local binary pattern gives rotational-invariance as well as lighting-invariance for robust features under these conditions. Various binning strategies help keep the inherent structure embedded in the features, which provide enough information for robust detection of the humans in the scene. The features are then classified using an adaboost classifier to provide a tree like structure for detection in multiple scales. The algorithm has been trained and tested on IR imagery and has been found to be fairly robust to viewpoint changes and lighting changes in dynamic backgrounds and visual scenes.