  Currently, studying the vehicle-human interactive behavior in the emergency
needs a large amount of datasets in the actual emergent situations that are
almost unavailable. Existing public data sources on autonomous vehicles (AVs)
mainly focus either on the normal driving scenarios or on emergency situations
without human involvement. To fill this gap and facilitate related research,
this paper provides a new yet convenient way to extract the interactive
behavior data (i.e., the trajectories of vehicles and humans) from actual
accident videos that were captured by both the surveillance cameras and driving
recorders. The main challenge for data extraction from real-time accident video
lies in the fact that the recording cameras are un-calibrated and the angles of
surveillance are unknown. The approach proposed in this paper employs image
processing to obtain a new perspective which is different from the original
video's perspective. Meanwhile, we manually detect and mark object feature
points in each image frame. In order to acquire a gradient of reference ratios,
a geometric model is implemented in the analysis of reference pixel value, and
the feature points are then scaled to the object trajectory based on the
gradient of ratios. The generated trajectories not only restore the object
movements completely but also reflect changes in vehicle velocity and rotation
based on the feature points distributions.
