Real-Time Reconstruction of Multi-Body Pedestrian Pre-Impact Posture in Collision Accidents From Monocular Images

Pedestrian kinematics is one of the important influence factors in pedestrian-vehicle collision accidents. The degree of pedestrian injury after collision is related to the initial pre-impact posture. The current research on pedestrian injury is based on the simulation collision of numerical pedestrian model, whose pre-impact posture comes from fixed gait sequence or manual construction. However, the former cannot represent the pedestrian posture of real accidents, and the latter takes a lot of time. This paper proposes an end-to-end framework to reconstruct pedestrian pre-impact posture from real-world accident images or videos for collision damage assessment. In order to obtain the most realistic pedestrian pre-impact posture, we first construct a video dataset of pedestrian-vehicle collision accidents. Then we use a human body 3D pose reconstruction method based on deep learning, SPIN (SMPL oPtimization IN the loop), which extracts the pose and body shape parameters of pedestrians in the image to generate SMPL (Skinned Multi-Person Linear) model. We exploit its similarity to the multi-body model to reconstruct the pre-impact pose of pedestrians. Experimental results show that our method can shorten the reconstruction time from minutes (327.25s) to milliseconds (0.260s), and the average error of joint angle is less than 3%. Through the computational simulation collision test, the collision damage results of the dummy constructed by our method is consistent with that of the real posture and the manually constructed posture. The proposed method is simple and easy to implement, which is helpful to enhance the performance of active and passive protection for pedestrians.