Unconscious Behavior Detection for Pedestrian Safety Based on Gesture Features
Yaru Dong, Yidong Li, Wenhua Liu, Jun Wu · 2017
Behavior detection is an important research field in pattern recognition and it can be applied to control the traffic safety. The mobile phones becomes a potential security when the pedestrians unconsciously use the phones during the crossing of the street. To avoid the traffic accidents, this paper proposes a new algorithm of the pedestrian behavior detection for the people unconsciously using phones. Firstly, the method that pedestrian detection based on gradient and texture feature integration is used to find the position of pedestrian. Secondly, Selective search is used to get the position of sensitive parts. We choose the arms as the sensitive parts in this algorithm. Finally, we extract and classify sensitive parts. Currently, fewer people are studying this topic. Therefore, we construct a new pedestrian image set that contains 2.5G images called PWUM (Pedestrian Who use mobile phone) set for verifying the effectiveness of our algorithm. Experimental results show that the proposed algorithm can efficiently detect pedestrian who is using mobile phone on PWUM dataset.