A Comparative Study of Outlier Detection Algorithms
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A unified framework for object retrieval and mining
IEEE Transactions on Circuits and Systems for Video Technology
Movement Detection and Tracking Using Video Frames
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Video object mining with local region tracking
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
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This paper addresses the special applications of datamining techniques in homeland defense. The problemtargeted, which is frequently encountered in military/intelligence surveillance, is to mine a massive surveillancevideo database automatically collected to retrieve theshots containing independently moving targets. A novelsolution to this problem is presented in this paper, whichoffers a completely qualitative approach to solving for theautomatic independent motion detection problem directlyfrom the compressed surveillance video in a faster thanrealtime mining performance. This approach is based onthe linear system consistency analysis, and consequentlyis called QLS. SincetheQLS approach only focuses onwhat exactly is necessary to compute a solution, it savesthe computation to a minimum and achieves the efficacy tothe maximum. Evaluations from real data show that QLSdelivers effective mining performance at the achieved efficiency.