The Recognition of Human Movement Using Temporal Templates
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The Trace Transform and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
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Recognizing Human Actions: A Local SVM Approach
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A face authentication system using the trace transform
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Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor
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Successive Convex Matching for Action Detection
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Behavior recognition via sparse spatio-temporal features
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
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Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners
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Human action recognition using distribution of oriented rectangular patches
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Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Learning actions using robust string kernels
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
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HBU'10 Proceedings of the First international conference on Human behavior understanding
Recognizing Human Actions Using Key Poses
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View-Independent Action Recognition from Temporal Self-Similarities
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Spatiotemporal salient points for visual recognition of human actions
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Gait Recognition Using Radon Transform and Linear Discriminant Analysis
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Machine based human action recognition has become very popular in the last decade. Automatic unattended surveillance systems, interactive video games, machine learning and robotics are only few of the areas that involve human action recognition. This paper examines the capability of a known transform, the so-called Trace, for human action recognition and proposes two new feature extraction methods based on the specific transform. The first method extracts Trace transforms from binarized silhouettes, representing different stages of a single action period. A final history template composed from the above transforms, represents the whole sequence containing much of the valuable spatio-temporal information contained in a human action. The second, involves Trace for the construction of a set of invariant features that represent the action sequence and can cope with variations usually appeared in video capturing. The specific method takes advantage of the natural specifications of the Trace transform, to produce noise robust features that are invariant to translation, rotation, scaling and are effective, simple and fast to create. Classification experiments performed on two well known and challenging action datasets (KTH and Weizmann) using Radial Basis Function (RBF) Kernel SVM provided very competitive results indicating the potentials of the proposed techniques.