A concurrent neural network approach to pedestrian detection in thermal imagery

The paper presents an original approach for pedestrian detection in thermal imagery using the neural network classifier called Concurrent Self-Organizing Maps (CSOM), previously introduced by first author; it represents a winner-takes-all collection of neural modules. The considered algorithm has the following main stages: (a) detection of the regions of interest (ROI); (b) feature selection using the Histogram of Oriented Gradients (HOG); (c) classification using a CSOM classifier with several neural modules for each class; (d) decision fusion of the SOM modules into the two final classes: pedestrians and non-pedestrians. For training and testing the proposed algorithm, we have used the OTCBVS-OSU Thermal Pedestrian Database provided by the Ohio State University. One obtains the False Positive Error Rate (FPER) of 1.79%, the False Negative Error Rate (FNER) of 0.49% and the Total Success Rate (TSR) of 98.48 %.