Human motion analysis: a review
Computer Vision and Image Understanding
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Representation and Recognition of Events in Surveillance Video Using Petri Nets
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Identification of humans using gait
IEEE Transactions on Image Processing
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
Detecting stochastically scheduled activities in video
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Recognizing action primitives in complex actions using hidden markov models
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition
International Journal of Computer Vision
| Hi-index | 0.00 |
Many activities may be characterized by a sequence of key frames that are related to important changes in motion rather than dominant characteristics that persist over a long sequence of frames. To detect such changes, we define a transformation operator at every time instant, which relates the past to the future states. One of the useful quantities associated with numerical range of an operator is the eigenvalue. In the literature, eigenvalue-based approaches have been studied extensively for many modeling tasks. These rely on gross properties of the data and are not suitable to detect subtle changes. We propose an antieigenvalue – based measure to detect key frames. Antieigenvalues depend critically on the turning of the operator, whereas eigenvalues represent the amount of dilation along the eigenvector directions aligned with the direction of maximum variance. We demonstrate its application to activity modeling and recognition using two datasets: a motion capture dataset and the UCF human action dataset.