Human Detection in Thermal Images Using Low-level Features

In this work the human detection method in infrared has been presented. The proposed solution focuses on the use low-level features and detecting parts of the human body. Low–level processing is based on modified HOG (Histogram of Oriented Gradients) algorithm. First, the only squared cells have been used, also calculation of the gradient has been improved. Next, the model of the head from the dataset IR (Infra Red) images has been created, also the model of the human body. Finally, the probability matrix has been examined using minimal distance classifier. The novelty of the proposed solution focuses on the combination of the pixel-gradient and body parts processing, also three stage classification process (head modelling, human modelling and classifier), which has been proposed to reduce the false detection. The experiments were performed on selfcreated IR images database, which contains images with most of the possible difficult situations such as overlapped people, different pose, small and high resolution of the people. The performance of the proposed algorithm was evaluated using Precision and Recall quality measure.