Moving objects detection at an intersection by sequential background extraction
Shinichiro Sonoda, Joo Kooi Tan, Hyoung Seop Kim, Seiji Ishikawa, Takashi Morie · International Conference on Control, Automation and Systems · 2011
Recently, there are many traffic accidents in turning right at an intersection. They are mainly caused by a driver's oversight of pedestrians and motorcycles that are occluded by oncoming cars. Therefore a system is necessary to detect moving objects such as oncoming cars and pedestrians at an intersection, and warn a vehicle driver. This paper describes a technique for detecting moving objects in turning right at an intersection when vehicle is stopping. Moving objects are detected by Mixture of Gaussians (MoG). In addition, we distinguish cars from pedestrians using the difference of the area size and the aspect ratio of detected objects. The object which is classified as a pedestrian is tracked using Lucas-Kanade Tracker. If the detected cars and pedestrians overlap or a car completely obscures pedestrians, we perform the estimation of pedestrian's location by using the information on past frames. By doing this, it is possible to detect pedestrians that drivers are actually difficult to see. The performance of the proposed technique was examined employing car videos and satisfactory results were obtained.