Multi-Vehicle Speed Estimation Algorithm Based on Real-Time Inter-Frame Tracking Technique
Keywords:Computer vision, image processing, inter-frame differencing, vehicle tracking, road accident
Inappropriate vehicle speeding remains a central factor that causes road accidents claiming millions of lives every year. This challenge has raised concerns for vehicle speed estimation as an attempt to promote speed enforcement methods. Traditionally, radar and lidar systems have widely been used for this purpose, despite their several shortfalls: cosine error effects, need for direct line-of-sight, and inability to simultaneously and accurately measure speed from multiple vehicles. The current work proposes an algorithm and a multi-vehicle speed estimation system in a multi-lane road environment to address multi-vehicle speed estimation shortfalls. The proposed solution exploits image processing and computer vision techniques to flag vehicles with inappropriate speeding patterns. A series of experiments showed that the developed system generates more accurate results than those given by the lidar system. In essence, the proposed system can estimate the speed of up to six vehicles concurrently. It can produce an average percentage error of 2.7% relative to the actual speed measured by a speedometer. This error is 5.4% lower than that demonstrated by the lidar system, emphasizing that the proposed system may be a more suitable approach to traffic laws enforcement.
Keywords: Computer vision, image processing, inter-frame differencing, vehicle tracking, road accident.