Pedestrian detection algorithm for on-board cameras of multi view angles

In this paper, a general algorithm for pedestrian detection by on-board monocular camera which can be applied to cameras of various view ranges in unified manner. The Spatio-Temporal MRF model extracts and tracks foreground objects as pedestrians and non-pedestrian distinguishing from background scenes as buildings by referring to motion difference. During the tracking sequences, cascaded HOG classifiers classify the foreground objects into the two classes of pedestrians and non-pedestrians. Before the classification, geometrical constraints on the relationship between heights and positions of the objects are examined to exclude the non-pedestrian objects. This pre-processing contributed to reducing the processing time of the classification while maintaining the classification accuracy. Due to the benefit of the tracking that the classifier can make decision totally considering Regions of Interest (ROIs) with same ID during consecutive images, this algorithm can operates quite robustly against noises and classification errors at each image frame.