Egocentric Analysis of Dash-Cam Videos for Vehicle Forensics

Abstract

Video acquisition using dashboard-mounted cameras has recently achieved massive popularity around the world. One of the major developments following the dash-cam's popularity is that videos captured by them can be used as testimony during scenarios, like traffic violations and accidents. The widespread deployment of dash-cams brings new problems ranging from the compromise of privacy by uploading these videos on public websites using videos captured from other cars for making fraudulent claims. Therefore, there is a compelling need to address the problems associated with the usage of dash-cam videos. In this paper, we discuss and highlight the importance of the emerging area of multimedia vehicle forensics. We propose an algorithm for linking a dash-cam video to a specific car. The proposed algorithm is useful for various applications, for example, insurance companies can authenticate the origin of video before processing the claim. In a different scenario of illegitimate video upload on the Web, the video can be traced back to the car it originated from. To this end, we make use of motion blur extracted from dash-cam videos for generating a discriminative feature. We observe that the subtle motion pattern of every vehicle can serve as its unique signature. We extract motion blur from dash-cam videos and use random forest trees for classifying the vehicle correctly. The experimental results on thousands of frames obtained from dash-cam videos of several cars show the effectiveness of our approach. We further investigate the process of forging the signature of a car and propose a counter forensics method to detect such forgery. Also, we discuss the application of our technique to other potential platforms where the camera can be mounted, for example, on the chest of a person. We believe that ours is the first work that describes this new area of research.