Moving object detection using motion field constraint with observer motion parameter

In this paper we propose a method for detecting moving objects in image sequence observed from a moving platform using optical flow. This problem is difficult because moving observer (i.e. camera) causes apparent motion in the image even for stationary environment. The method can be applied to many situations, such as a robot vision and an obstacle detection for an autonomous vehicle system. We assume that observer motion parameter (translation and rotation) is known and image system is modeled by perspective projection. For the problem, some methods have been proposed, in which the complex logarithm mapping, the estimation of Focus of Expansion and the depth of objects are used. For a given motion parameter of camera, we can formulate motion field constraint (MFC) in the image plane which is satisfied by the relative movement of stationary environment against camera motion. On the other hand, the motion vector in the image plane, which is called motion field, is estimated by the well-known optical flow constraint (OFC). Our main idea is to use the difference between two estimation results. One is the solution of minimizing least squared OFC subjected with MFC, and the other is the solution of that without MFC. For the stationary environment region, the difference between two is small and the difference tends to be large at the moving region. Therefore, the suitable criterion for these values will separate two regions precisely. In our study, two criteria are proposed and are investigated. One criterion uses squared residual of OFC with and without MFC. Another criterion uses directional error between two solutions. The validity of our method is shown through some examples, and the obtained results show the latter criterion gives more accurate estimation than the former one.