Novel outline features for pedestrian detection system with thermal images

Recently, the need of pedestrian detection at night has gained more and more interest. However, the performance of traditional nighttime pedestrian detection systems remains poor because region of interest (ROI) generation and feature extraction are designed separately. Thus, this paper presents novel thermal imagery algorithms to enhance the performance of nighttime pedestrian detection systems. The proposed thermal image pedestrian detection system involves novel outline features, developed from the ROI generation method of pedestrians that are different from traditional features. A three-layer back-propagation feed-forward neural network is used as the classifier. Two databases, the OTCBVS database and our own are used to evaluate the performance of the proposed thermal image pedestrian detection algorithm. Experimental results show that the proposed outline features are effective, and the detection performance of a traditional pedestrian detection system at night is improved.