Abstract:

The human body target detection research is a research hotspot in the field of computer vision in recent years.In view of the poor pedestrian detection accuracy,this paper presents an efficient pedestrian detection algorithm.Different types of local features HOG and LBP are selected and filtered by the first stage Real AdaBoost algorithm,after which the co-occurrence probability features are generated by pairwise.Finally,weak classifiers are transformed into a strong recognizer to detect pedestrians through the second stage of the Real AdaBoost algorithm.Experiment in OpenCV and VS2010 shows that the algorithm can better detect pedestrian and improve the pedestrian detection accuracy and robustness compared with the OpenCV buit-in algorithm.

Key words: HOG;LBP;co-occurrence probability feature;Real AdaBoost;OpenCV VS2010