Online person orientation estimation based on classifier update
Hong Liu, Liqian Ma · 2015
Person orientation estimation is valuable for intelligent video surveillance. Although much progress has been made in recent years, it still faces challenges such as varying poses, illuminations and viewpoints. Most existing approaches merely use appearance information or combine it with motion information. Appearance-based classifiers are trained offline without updating in real time, which can not adapt to unknown scenes. To fix it, a novel orientation estimation approach based on online appearance-based classifier update is proposed. Reliable motion direction is determined acting as pre-estimated person orientation to update the appearance-based classifier. Moreover, a novel criterion based on motion reliability is proposed to determine the motion direction. Experimental results show that the proposed approach achieves more competitive performances especially for unknown scenes.