Road Segment Re-Identification in Dashcam Videos

Due to the widespread of dashcams, we will have more videos that capture roads/streets in driving. Consequently, a vast amount of the videos will be available and can be utilized for analyzing road safety and similar purposes. For example, suppose different dashcams can take vehicle/pedestrian traffic at a risky intersection at different timings. In that case, the collection of such videos will effectively recognize the cause of dangerous situations without surveillance camera infrastructure. However, identifying a Road Segment of Interest (RSI) in the video, such as near the intersection region, is challenging as the video frames do not usually include location tags. In this paper, we present a unique approach to attack this challenge. Assume that a video segment, called reference video, captures an RSI. We re-identify the RSI taken in another video (called test video) that captures the roads containing that RSI. By this approach, we can automatically extract the video segment corresponding to RSI from a given test video, using the reference video. We introduce AKAZE features to assess frame-level similarity and develop an algorithm to find frame-by-frame matching between reference and test videos. We have evaluated our method using 10 reference videos that correspond to 10 RSIs, each with 5 test videos. The result has shown that the average frame error distance was only 3.03 in daytime and 4.93 in nighttime, which are sufficiently low to re-identify RSI in the newly obtained test videos.