Abstract: The automotive industry invests substantial amounts of money in driver-security and driver-assistance systems. We propose an overtaking detection system based on visual motion cues that combines feature extraction, optical flow, solid-objects segmentation and geometry filtering, working with a low-cost compact architecture based on one focal plane and an on-chip embedded processor. The processing is divided into two stages: firstly analog processing on the focal plane processor dedicated to image conditioning and relevant image-structure selection, and secondly, vehicle tracking and warning-signal generation by optical flow, using a simple digital microcontroller. Our model can detect an approaching vehicle (multiple-lane overtaking scenarios) and warn the driver about the risk of changing lanes. Thanks to the use of tightly coupled analog and digital processors, the system is able to perform this complex task in real time with very constrained computing resources. The proposed method has been validated with a sequence of more than 15,000 frames (90 overtaking maneuvers) and is effective under different traffic situations, as well as weather and illumination conditions.
Journal: Machine Vision and Applications
As the name implies, the system detects detects overtaking vehicles based on optical-flow. Robustness of the system has been tested using more than 15,000 frames under realistic illumination conditions. The figure below represents different sub-tasks implemented in the system.
As it can be observed from the figure above, optical-flow due to egomotion (movement of the camera itself) needs to be filtered out in order to eliminate false detections. The video below demonstrates performance of the system using real footage from traffic: