HOG feature human detection system
Matt Davis, Ferat Sahin · 2016
Human detection systems are becoming more important as more automatic and robotic systems are being used in the world. RGB, depth, and thermal images can be used together to produce a better detection system that works in situations where one of the sensors might not produce valuable data. HOG features can provide valuable information for detecting humans in an image and that data can be used to train individual classifiers to detect humans in a scene. The combination of sensor modalities in conjunction with individual classifiers can create a human detection system that can detect partially obscured humans. The multi-layer classifier that was created provided a high level of accuracy when tested against untrained data. The multi-layer classifier performed better than eleven of the twelve individual classifiers, but did not overcome the SVM thermal HOG classifier. The multi-layer classifier had a much tighter standard deviation and fell within the band of the SVM thermal classifier.