Approaching car detection via clustering of vertical-horizontal line scanning optical edge flow

Abstract

Here, we describe a method that detects vehicle(s) approaching from behind to a commuting car in the lane in which both are travelling. This research contributes to the development of driver assistance systems by means of informing them about the approaching traffic from behind and warn the drivers in case they are drowsy or not alert and the driving conditions are hazardous. We use the image pairs extracted from a video clip obtained from a video camera mounted on the back side of the car. This allows detection of the moving objects from the video image pairs using optical flow. Objects which are determined as not cars or vehicles have been eliminated by edge extraction. In turn, this approach leads to lessen the operation processing cost. Then, Density Histogram of Cluster Rows (DHCR) and Density Histogram of Cluster Columns (DHCC) are generated for the purpose of classification of motion vectors (MVs). Consequently, approching vehicles and cars are detected by localizing the place of the motion vector clusters using Vertical Horizontal Line Scanning (VHLS) as experimental results demonstrate.