Robust and efficient change detection algorithm based on 3D line segments

In this paper we examine and improve a new approach for change detection (introduced in [1]) which is based on the appearance and disappearance of 3D line segments as seen in a new image. These 3D line segments are estimated from a set of learning images taken from arbitrary viewpoints and under arbitrary light conditions in an unsupervised manner. The main advantage of the proposed method lies in the fact that the change detection is performed by comparing line segments, and not surfaces or gray levels. Computing 3D surfaces in an image can be computationally intensive, and other methods such as image subtraction or cross-correlation are sensitive to lighting conditions and changes in viewpoints. Moreover, most man-made objects such as buildings, cars, and even cities viewed from above consist mainly of straight linesman-made objects, and therefore this method is highly applicable for such structures. The proposed algorithm first focuses on the reconstruction of a set of 3D line segments forming a certain 3D scene using a set of 2D line segments obtained from the learning images in an unsupervised manner, without any prior knowledge on the cameras' positions or relative distance. In the change detection stage, we use the reconstructed 3D scene of line segments to check if changes, such as appearance or disappearance of objects, have occurred in a given test image. This test image can be taken from arbitrary viewpoint and under arbitrary lighting conditions. Our change detection algorithm not only distinguishes between the states of "changed" and "not-changed" line segments, it also classifies the "changed" line segments as appeared - objects that entered the scene in the test image, and disappeared - objects that left the 3D scene reconstructed from the lines of the learning images.