Recognition and pose estimation of urban road users from on-board camera for collision avoidance

Collision avoidance systems are not only required to detect road users around vehicles, but also expected to understand and predict the behavior of road users for risk assessment. This paper focuses on two kinds of similar road users, pedestrian and cyclist, and proposes a behavior analysis framework. The proposed method firstly recognizes the type of road user, and then estimates the pose of road user. The first recognition phase employs a cascade structured classifier. This classifier distinguishes cyclist from pedestrian using multiple features and discriminative local area, in order to achieve a high recognition rate. In the second pose estimation phase, both head orientation and body orientation are estimated. In order to obtain more accurate classifier, Semi-Supervised Learning is applied instead of the conventional Supervised Learning method for training. Moreover, the human physical model constraint and temporal constraint are considered, which assist the pose estimation to produce reasonable and stable result in video sequence. A series of experiments demonstrate the effectiveness of the proposed method.