Reliability of motion features in surveillance videos
Integrated Computer-Aided Engineering - Performance Metrics for Intelligent Systems
Texture Dissimilarity Measures for Background Change Detection
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Multi-layer Background Change Detection Based on Spatiotemporal Texture Projections
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Detection of moving objects using incremental connectivity outlier factor algorithm
Proceedings of the 47th Annual Southeast Regional Conference
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ICVS'08 Proceedings of the 6th international conference on Computer vision systems
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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.