Missing Frame Detection of Surveillance Videos Based on Deep Learning in Forensic Science

The identification of road traffic accidents plays an essential role in the litigation process of complicated traffic cases. Speed appraisement is the main part among all kinds of judicial identification in the area of road traffic accident. When evaluating the speed of vehicles, video frame time is an important parameter. The situation of missing frames may cause an imprecision of speed inspection. In this paper, we propose a method to detect missing frames by the movement of objects in video based on deep learning techniques. The method is based on object detection neural network. A derived distance of target object is calculated and applied to detect missing frames. We then confirm the performance of proposed method on dataset consisted of collected surveillance videos. It can find missing frames accurately and rapidly, which effectively reduces calculation errors of vehicle speed and promotes the authenticity of forensic investigation.