Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Human motion recognition using an eigenspace
Pattern Recognition Letters
High-Speed Human Motion Recognition Based on a Motion History Image and an Eigenspace
IEICE - Transactions on Information and Systems
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A novel human motion recognition method based on eigenspace
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
On Appearance Based Face and Facial Action Tracking
IEEE Transactions on Circuits and Systems for Video Technology
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This paper proposes a robust appearance-based method for recognizing directed human activities with scale variation based on a compound eigenspace. The method addresses two main issues associated with activity recognition when a human is moving away from or closer to the cameras. The first issue is the variation in human silhouette sizes as a result of object-camera distance changes. The second is the insufficient information of shape and speed of the limbs due to self occlusions. An eigenvector-based linear algorithm is employed for dimensionality reduction and activity recognition here. In addition to the conventional data available in each video frame, our method extracts two more pieces of information that are used to control the recognition process. In particular, the use of a compound eigenspace, controlled by the silhouette's relative speed and linear displacement vector, has clearly improved the recognition. The method has been trained and tested using the four scenarios of the KTH dataset, which contains hundreds of videos partitioned into six human activities.