An evaluation on robustness and brittleness of HOG features of human detection

Detecting humans in an image sequence is one of the most difficult problems in object recognition. It is necessary to define a robust descriptor which can extract human features from images, to improve the detecting performance. Histograms of Oriented Gradients(HOG) descriptor significantly outperforms compared with the others on human detection. The descriptor is known as a robustness descriptor for illumination changes and geometrical changes in local regions. To obtain the high detection performance using the descriptor effectively, it is necessary to know the robustness and brittleness of the descriptor. In this paper, we experiment the descriptor to verify its robustness to illumination changes and to scrutinize its brittleness. For the experiments, we use LogitBoost which can create a human-detector by learning human features.