The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fully Complex Multi-Layer Perceptron Network for Nonlinear Signal Processing
Journal of VLSI Signal Processing Systems
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Orthogonality of decision boundaries in complex-valued neural networks
Neural Computation
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A differential geometric approach to representing the human actions
Computer Vision and Image Understanding
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Action Recognition Using LBP-TOP as Sparse Spatio-Temporal Feature Descriptor
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action Recognition in Video by Covariance Matching of Silhouette Tunnels
SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
Letters: Fully complex extreme learning machine
Neurocomputing
A survey on vision-based human action recognition
Image and Vision Computing
The computational power of complex-valued neuron
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Action recognition for surveillance applications using optic flow and SVM
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Feature detector and descriptor evaluation in human action recognition
Proceedings of the ACM International Conference on Image and Video Retrieval
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Action recognition in video by sparse representation on covariance manifolds of silhouette tunnels
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
View-invariant modeling and recognition of human actions using grammars
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Fast learning fully complex-valued classifiers for real-valued classification problems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Information Sciences: an International Journal
A Sequential Learning Algorithm for Complex-Valued Self-Regulating Resource Allocation Network-CSRAN
IEEE Transactions on Neural Networks
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In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, ''fast learning fully complex-valued neural (FLFCN) classifier'' is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier.