Motion Detection Based on Local Variation of Spatiotemporal Texture

Abstract

In this paper we propose to use local variation of spatiotemporal texture vectors for motion detection. The local variation is defined as the largest eigenvalue component of spatiotemporal (sp) texture vectors in certain time window at each location in a video plane. Sp texture vectors are computed using a dimensionality reduction technique applied to spatiotemporal (3D) blocks. They provide a compact vector representation of texture and motion patterns for each block. The fact that we go away from the standard input of pixel values and instead base the motion detection on sp texture of 3D blocks, significantly improves the quality of motion detection. This is particularly relevant for infrared videos, where pixel values have smaller range than in daylight color or gray level videos.