Fuzzy lattice neurocomputing (FLN) models
Neural Networks
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predictive Modular Neural Networks: Applications to Time Series
Predictive Modular Neural Networks: Applications to Time Series
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Bayesian Approach to Human Activity Recognition
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Successive Convex Matching for Action Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Towards a Unified Modeling and Knowledge-Representation based on Lattice Theory: Computational Intelligence and Soft Computing Applications (Studies in Computational Intelligence)
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Fast and accurate global motion estimation algorithm using pixel subsampling
Information Sciences: an International Journal
Real-time human action recognition by luminance field trajectory analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Online, Real-time Tracking and Recognition of Human Actions
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Statistical descriptors for human actions classification
MED '09 Proceedings of the 2009 17th Mediterranean Conference on Control and Automation
Human action recognition using distribution of oriented rectangular patches
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Trajectory-based representation of human actions
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Motion detection using wavelet analysis and hierarchical markov models
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Local descriptors for spatio-temporal recognition
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Rough set theory applied to lattice theory
Information Sciences: an International Journal
Information Sciences: an International Journal
Recognizing architecture styles by hierarchical sparse coding of blocklets
Information Sciences: an International Journal
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This work describes a computational approach for a typical machine-vision application, that of human action recognition from video streams. We present a method that has the following advantages: (a) no human intervention in pre-processing stages, (b) a reduced feature set, (c) modularity of the recognition system and (d) control of the model's complexity in acceptable for real-time operation levels. Representation of each video frame and feature extraction procedure are formulated in the lattice theory context. The recognition system consists of two components: an ensemble of neural network predictors which correspond to the training video sequences and one classifier, based on the PREMONN approach, capable of deciding at each time instant which known video source has potentially generated a new sequence of frames. Extensive experimental study on three well known benchmarks validates the flexibility and robustness of the proposed approach.