Arm-hand behaviours modelling: from attention to imitation

Authors:
Sean R. F. Fanello;Ilaria Gori;Fiora Pirri
Affiliations:
Sapienza Università di Roma, Dipartimento di Informatica e Sistemistica, Roma, RM, Italy;Sapienza Università di Roma, Dipartimento di Informatica e Sistemistica, Roma, RM, Italy;Sapienza Università di Roma, Dipartimento di Informatica e Sistemistica, Roma, RM, Italy
Venue:
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Year:
2010

Citing 11
Cited 1

Human motion analysis: a review

Computer Vision and Image Understanding
Introduction To Automata Theory, Languages, And Computation

Introduction To Automata Theory, Languages, And Computation
Qualitative Spatiotemporal Analysis Using an Oriented Energy Representation

ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Probabilistic Finite-State Machines-Part I

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational studies of human motion: part 1, tracking and motion synthesis

Foundations and Trends® in Computer Graphics and Vision
All of Nonparametric Statistics (Springer Texts in Statistics)

All of Nonparametric Statistics (Springer Texts in Statistics)
A survey of advances in vision-based human motion capture and analysis

Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A tutorial on spectral clustering

Statistics and Computing
Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms

Pattern Recognition
A survey on vision-based human action recognition

Image and Vision Computing
Gesture Recognition: A Survey

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Keep it simple and sparse: real-time action recognition

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a new and original method for modelling arm-hand actions, learning and recognition. We use an incremental approach to separate the arm-hand action recognition problem into three levels. The lower level exploits bottom-up attention to select the region of interest, and attention is specifically tuned towards human motion. The middle level serves to classify action primitives exploiting motion features as descriptors. Each of the primitives is modelled by a Mixture of Gaussian, and it is recognised by a complete, real time and robust recognition system. The higher level systemcombines sequences of primitives using deterministic finite automata. The contribution of the paper is a compositional based model for arm-hand behaviours allowing a robot to learn new actions in a one time shot demonstration of the action execution.