LAUSR

Abstract. Despite all the significant advances in human detection in various environmental conditions, it is still a challenging task. Most of the human detection algorithms mainly use color information, which is not robust to lighting changes and varying colors under which such a detector should operate namely day and nighttime. This problem is further amplified with infrared (IR) imagery, which only contains grayscale information. The proposed algorithm for human detection uses intensity distribution, gradient, and texture features for effective detection of humans in IR imagery. For the detection of intensity, histogram information is obtained in the grayscale channel. For extracting gradients, we utilize the histogram of oriented gradients for better information in the various lighting scenarios. For extracting 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.