Moving Object Segmentation Using Scene Understanding

Abstract

We present a novel approach to moving object detection in video taken from a translating, rotating and zooming sensor, with a focus on detecting very small objects in as few frames as possible. The primary innovation is to incorporate automatically computed scene understanding of the video directly into the motion segmentation process. Scene understanding provides spatial and semantic context that is used to improve frame-to-frame homography computation, as well as direct reduction of false alarms. The method can be applied to virtually any motion segmentation algorithm, and we explore its utility for three: frame differencing, tensor voting, and generalized PCA. The approach is especially effective on sequences with large scene depth and much parallax, as often occurs when the sensor is close to the scene. In one difficult sequence, our results show an 8-fold reduction of false positives on average, with essentially no impact on the true positive rate. We also show how scene understanding can be used to increase the accuracy of frame-to-frame homography estimates.