Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Activity Recognition Using Multidimensional Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion detection with nonstationary background
Machine Vision and Applications
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
A Reliable-Inference Framework for Recognition of Human Actions
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognition-based gesture spotting in video games
Pattern Recognition Letters
Speaker background models for connected digit password speaker verification
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Background model design for flexible and portable speaker verification systems
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Multi-agent activity recognition using observation decomposedhidden Markov models
Image and Vision Computing
Learning dynamics for exemplar-based gesture recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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This paper presents a new test to distinguish between meaningful and non-meaningful HMM-modeled activity patterns in human activity recognition systems. Operating as a hypothesis test, alternative models are generated from available classes and the decision is based on a likelihood ratio test (LRT). The proposed test differs from traditional LRTs in two aspects. Firstly, the likelihood ratio, which is called pairwise likelihood ratio (PLR), is based on each pair of HMMs. Models for non-meaningful patterns are not required. Secondly, the distribution of the likelihood ratios, rather than a fixed threshold, is used as the measurement. Multiple measurements from multiple PLR tests are combined to improve the rejection accuracy. The advantage of the proposed test is that the establishment of such a test relies only on the meaningful samples.