Multiple obstacle detection and tracking using stereo vision: Application and analysis

Vision systems provide a large functional spectrum for perception applications and, in recent years, they have demonstrated to be essential in the development of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles. In this context, this paper presents an on-road objects detection approach improved by our previous work in defining the traffic area and new strategy in obstacle extraction from U-disparity. Then, a modified particle filtering is proposed for multiple object tracking. The perception strategy of the proposed vision-only detection system is structured as follows : First, a method based on illuminant invariant image is employed at an early stage for free road space detection. A convex hull is then constructed to generate a region of interest (ROI) which includes the main traffic road area. Based on this ROI, an U-disparity map is built to characterize on-road obstacles. In this approach, connected regions extraction is applied for obstacles detection instead of standard Hough Transform. Finally, a modified particle filter framework is employed for multiple targets tracking based on the former detection results. Besides, multiple cues, such as obstacle's size verification and combination of redundant detections, are embedded in the system to improve its accuracy. Our experimental findings demonstrates that the system is effective and reliable when applied on different traffic video sequences from a public database.