The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
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FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Cubic Higher-Order Local Auto-Correlation (CHLAC) is feature vector that simultaneously represent motion and shape. The system learns a sample set of "usual motion" to create a "usual subspace" with PCA. Feature vectors are then similarly extracted from unknown input data, and accurate detection of "unusual motion" is achieved by measuring the deviation from the usual subspace. Therefore, by defining unusual motion as "motion that is outside the usual motion," this method can detect unusual motion without an actual model of unusual motion, which differs depending on the situation, and furthermore, is difficult to define. This paper reports on the fast CHLAC that we have developed, so that these capabilities of CHLAC can be put to practical use as an unusual motion detection system that operates in real time. This paper also demonstrated the effectiveness of this method through example tests, conducted using real images both indoors and outdoors.