On-board Pedestrian Detection by the Motion and the Cascaded Classifiers
This paper presents a general algorithm for pedestrian detection by on-board monocular camera, which can be applied to cameras of various viewing angle. The foreground objects which have discriminative motion-differences compared to that of background buildings are extracted as Regions of Interest (ROIs). Then, those ROIs are tracked by Spatio-Temporal MRF(S-T MRF) model as a possible pedestrian. Before classification process, some ROIs are rejected quickly by geometric constraints, calculated from coordinates between position of ROI and heights in real-world. Finally, ROIs are verified by HOG/Fisher cascade. The geometrical pre-processing saves the overall computational costs as well as decrease false positive rate. Also, algorithm compensates the detection rate due to temporal classification error in tracking cues, which improves the stability of tracking.
pedestrian detection tracking hog on-board vision sensor