3DSVHT: Extraction of 3D Linear Motion via Multi-view, Temporal Evidence Accumulation

Abstract

Shape recognition and motion estimation are two of the most difficult problems in computer vision, especially for arbitrary shapes undergoing severe occlusion. Much work has concentrated on tracking over short temporal scales and the analysis of 2D image-plane motion from a single camera. In contrast, in this paper we consider the global analysis of extended stereo image sequences and the extraction of specified objects undergoing linear motion in full 3D. We present a novel Hough Transform based algorithm that exploits both stereo geometry constraints and the invariance properties of the cross-ratio to accumulate evidence for a specified shape undergoing 3D linear motion (constant velocity or otherwise). The method significantly extends some of the ideas originally developed in the Velocity Hough Transform, VHT, where detection was limited to 2D image motion models. We call our method the 3D Stereo Velocity Hough Transform, 3DSVHT. We demonstrate 3DSVHT on both synthetic and real imagery and show that it is capable of detecting objects undergoing linear motion with large depth variation and in image sequences where there is significant object occlusion.