Improved pedestrian detection by adjustment of segmented ROI in thermal night vision

In this work we present an analysis of the region of interest (ROI) generation from the source thermal night vision images through the double thresholding segmentation technique as a part of pedestrian detection procedure. In some cases, pedestrians do not fit into the generated ROIs. To solve the problem we propose to adjust (slightly enlarge) the segmented ROI. Through this, it is possible to reduce miss rate for the aggregated channel feature (ACF) classifier from 29.1% to 24.8% and for the deep convolutional neural network (CNN) classifier from 24.0% to 22.4%, with negligible impact on the processing time.