Real-time moving object detection using a vehicle-mounted monocular rear-view fisheye camera

In this paper, we propose a moving object detection algorithm that enables to detect moving objects with collision risk in rear of the vehicle by using image sequences from a vehicle-mounted monocular rear-view fisheye camera. The proposed moving object detection algorithm detects corner points by using Harris corner detector and computes the optical flow vectors from two consecutive images corresponds to the detected corner points. By considering the feature of the vehicle movement that the vehicle goes straight in a short time interval, we find the focus of expansion (FOE) by using matched filter and divide the image into four sections around the FOE. The optical flow angle distribution of each section is analyzed to find pixels corresponds to the background components and robust background motion compensation method to the complex scene is suggested. Add to this, we propose false removal method that eliminates false positives by considering two features of the detection box for the moving object candidates; position and pixel intensity distribution. Simulation results show that our proposed algorithm achieves 97.19% of detection rate toward various detection target including pedestrians, bicycles, and cars. Furthermore, our proposed false removal algorithm performs an extremely low 2.7% of false rate toward false positives such as trees, shadows, and road markers.