Object segmentation from a dynamic background using a pixelwise rigidity criterion and application to maritime target recognition

This paper describes a novel approach for rigid object segmentation from a dynamic background using a pre-recorded video with a moving camera, and we apply it to the problem of vessel segmentation in a maritime video scene. The difficulty of modeling background appearance or/and dynamic, modeling object appearance, and compensating camera motion renders this task very challenging. Therefore, the proposed method only uses a geometric constraint regarding target motion rigidity in order to achieve object segmentation in a full video segment. Such an idea is not new in object segmentation literature, but the novelty in this paper resides in that the target rigidity assumption is implemented at the pixel level, but not at the object scale. This is firstly achieved by deriving a theoretical optic flow model in the neighborhood of each pixel under the 3D rigid motion assumption of object, which is later compared against the observed 2D optic flow model in the neighborhood of a pixel in order to derive a pixelwise rigidity criterion. The latter is further reenforced along individual (pixel) trajectories in a video segment. Finally, a mere thresholding operation allows to quickly extract target from background in a full video segment. Our experiments using real maritime video sequences captured with an airborne camera have shown that the method detects maritime targets accurately.