Moving Object Detection based on Reverse Projection Error Model

Abstract

A novel moving feature point detection method is proposed in this paper in order to solve the problems in existing methods. Firstly, the coordinates of feature points are normalized by using spherical coordinates in order to solve the imaging distortion of the fish-eye camera. Secondly, based on the spherical normalization coordinates of feature points the reverse projection error model of the fish-eye camera is deduced and a moving feature point’s discriminant is derived to improve the detection integrity of moving object. Finally, according to the law of object motion, the detected potential moving feature points are clustered to further remove outliers. Experimental results show the proposed moving feature point detection method has better detection results compared with the commonly used methods.