Fast human detection using Node-Combined Part Detector

Detecting people in occlusion and articulated pose remains a big challenging problem in computer vision. To achieve a fast and accurate human detection algorithm, Node-Combined Part Detector (NCPD) Model is proposed in this paper. We make two major contributions: (1) We propose a novel method, torso-nodes combination, to integrate part detectors. (2) We adopt stable part detectors described by Associated Paring Comparison Features (APCF) and trained with Real-AdaBoost algorithm. This new human detection algorithm is not only much faster than the previous work but also maintaining competitive accuracy with the state-of-the-art human detection system. Besides, the algorithm performs better within low false alarm. For average time per image, our algorithm can achieve speedup rate of about 10x as compared with Deformable Part based Model (DPM) and over 125x as compared with Poselet Model.