Pedestrian detection approach based on modified Haar-like features and AdaBoost

Pedestrian detection is an important task in many applications such as intelligent transportation systems, image retrieval, surveillance systems, automated personal assistance, etc. This paper proposes a set of modified Haar-like features that have parallelogram shapes. Using the proposed feature descriptors to develop a rapid detection system for pedestrian detection based on decision tree structure used boosting algorithm. The experimental results showed that the proposed method could produce high accuracy detection rate with lower false positive rate and higher recall rate than original Haar-like features and it is efficiency with different resolutions and gestures under a variety of backgrounds as well as lighting.