Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
User-independent online gesture recognition by relative motion extraction
Pattern Recognition Letters
Learning Pedestrian Models for Silhouette Refinement
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Gait shape estimation for identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
What image information is important in silhouette-based gait recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Directionally-grouped CHLAC motion feature extraction and its application to sport motion analysis
NEHIPISIC'11 Proceeding of 10th WSEAS international conference on electronics, hardware, wireless and optical communications, and 10th WSEAS international conference on signal processing, robotics and automation, and 3rd WSEAS international conference on nanotechnology, and 2nd WSEAS international conference on Plasma-fusion-nuclear physics
Efficient optimization for low-rank integrated bilinear classifiers
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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This paper presents a feature extraction method for three-way data: the cubic higher-order local auto-correlation (CHLAC) method. This method is particularly suitable for analysis of motion-image sequences. Motion-image sequences can be regarded as three-way data consisting of x-, y- and t-axes. The CHLAC method is based on three-way auto-correlations of pixels in motion images. It effectively extracts spatio-temporal local geometric features characterizing the motion, such as gradients (velocities) and curvatures (accelerations). It has also several advantages for motion recognition. Firstly, neither a priori knowledge nor heuristics about the objects in question is required. Secondly, it is shift-invariant and thus segmentation-free. Thirdly, its computational cost is less than that of traditional methods, which makes it more suitable for real time processing. The experimental results on large datasets for gesture and gait recognition showed the effectiveness of the CHLAC method.