Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of the Self-Organizing Map to Trajectory Classification
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Gait-Based Recognition of Humans Using Continuous HMMs
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
A hierarchical self-organizing approach for learning the patterns of motion trajectories
IEEE Transactions on Neural Networks
CCTV Video Analytics: Recent Advances and Limitations
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
HMM based action recognition with projection histogram features
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Background subtraction for automated multisensor surveillance: a comprehensive review
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Improving the accuracy of action classification using view-dependent context information
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Accelerometry-based classification of human activities using Markov Modeling
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
An efficient approach for multi-view human action recognition based on bag-of-key-poses
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Common-Sense knowledge for a computer vision system for human action recognition
IWAAL'12 Proceedings of the 4th international conference on Ambient Assisted Living and Home Care
A survey of video datasets for human action and activity recognition
Computer Vision and Image Understanding
Human action recognition optimization based on evolutionary feature subset selection
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Common-sense reasoning for human action recognition
Pattern Recognition Letters
Silhouette-based human action recognition using sequences of key poses
Pattern Recognition Letters
| Hi-index | 0.00 |
This paper addresses the problem of silhouette-based human action modeling and recognition, specially when the number of action samples is scarce. The first step of the proposed system is the 2D modeling of human actions based on motion templates, by means of Motion History Images (MHI). These templates are projected into a new subspace using the Kohonen Self Organizing feature Map (SOM), which groups viewpoint (spatial) and movement (temporal) in a principal manifold, and models the high dimensional space of static templates.The next step is based on the Hidden Markov Models (HMM) in order to track the map behavior on the temporal sequences of MHI. Every new MHI pattern is compared with the features map obtained during the training. The index of the winner neuron is considered as discrete observation for the HMM. If the number of samples is not enough, a sampling technique, the Sampling Importance Resampling (SIR) algorithm, is applied in order to increase the number of observations for the HMM. Finally, temporal pattern recognition is accomplished by a Maximum Likelihood (ML) classifier. We demonstrate this approach on two publicly available dataset: one based on real actors and another one based on virtual actors.