Concurrent Self-organizing Maps for Pedestrian Detection in Thermal Imagery

The paper presents an original approach for pedestrian detection in thermal imagery using Histogram of Oriented Gradients (HOG) for feature extraction and the neural network classifier called Concurrent Self-Organizing Maps (CSOM), previously introduced by first author. The proposed 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. After optimizing HOG descriptors parameters we 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 %.