Multi-branch classifiers for pedestrian detection from infrared night and day images

This paper presents a modified multi-branch classifier of pedestrians from far infrared (FIR) night and day images. The solution is accurate, fast, and especially best suited for all realtime applications where pedestrians may appear in many distances to the camera, like in cars and CCTV. Two proposed training methods of the classifier, namely full-scale and partial-scale training were deeply tested. Results show increased efficiency of the classification process (by up to 3 %) with similar processing time in comparison to a single classifier. All tests were conducted using Adaboost classifier, but generally, the results should be consistent for other classifiers.