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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Space-Time Shapelets for Action Recognition
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
A new shape descriptor defined on the Radon transform
Computer Vision and Image Understanding
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
A general regression neural network
IEEE Transactions on Neural Networks
A survey of video datasets for human action and activity recognition
Computer Vision and Image Understanding
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We propose a framework for recognizing actions or gestures by modelling variations of the corresponding shape postures with respect to each action class thereby removing the need for normalization for the speed of motion. The three main aspects are the shape descriptor suitable for describing its posture, the formation of a suitable posture space, and a regression mechanism to model the posture variations with respect to each action class. Histogram of gradients(HOG) is used as the shape descriptor with the variations being mapped to a reduced Eigenspace by PCA. The mapping of each action class from the HOG space to the reduced Eigen space is done using GRNN. Classification is performed by comparing the points on the Eigen space to those determined by each of the action model using Mahalanobis distance. The framework is evaluated on Weizmann action dataset and Cambridge Hand Gesture dataset providing significant and positive results.