Human motion analysis: a review
Computer Vision and Image Understanding
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Appearance-Based Representation of Action
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Simplest Representation Yet for Gait Recognition: Averaged Silhouette
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
Matching Shape Sequences in Video with Applications in Human Movement Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Activity recognition by integrating the physics of motion with a neuromorphic model of perception
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Physics-based activity modelling in phase space
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Learning features of intermediate complexity for the recognition of biological motion
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Identification of humans using gait
IEEE Transactions on Image Processing
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Physics-based activity modelling in phase space
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
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Recognizing a person's motion is intuitive for humans but represents a challenging problem in machine vision. In this paper, we present a multi-disciplinary framework for recognizing human actions. We develop a novel descriptor, the Human Action Image (HAI), a physically-significant, compact representation for the motion of a person, which we derive from Hamilton's Action. We prove the additivity of Hamilton's Action in order to formulate the HAI and then embed the HAI as the Motion Energy Pathway of the Neuro-biological model of motion recognition. The Form Pathway is modelled using existing low-level feature descriptors based on shape and appearance. Finally, we propose a Weighted Integration (WI) methodology to combine the two pathways via statistical Hypothesis Testing using the bootstrap to do the final recognition. Experimental validation of the theory is provided on the well-known Weizmann and USF Gait datasets.