Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Charade: remote control of objects using free-hand gestures
Communications of the ACM - Special issue on computer augmented environments: back to the real world
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Interacting with Virtual Humans through Body Actions
IEEE Computer Graphics and Applications
Gesture Modeling and Recognition Using Finite State Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A Bayesian Approach to Human Activity Recognition
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
Probabilistic Motion Parameter Models for Human Activity Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Real-time Analysis of Data from Many Sensors with Neural Networks
ISWC '01 Proceedings of the 5th IEEE International Symposium on Wearable Computers
Constructing Finite State Machines for Fast Gesture Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
A Hybrid Stochastic-Connectionist Architecture for Gesture Recognition
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Pointing gesture recognition based on 3D-tracking of face, hands and head orientation
Proceedings of the 5th international conference on Multimodal interfaces
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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The research presented here makes a contribution to the understanding of the recognition of biological motion by comparing human recognition of a set of everyday gestures and motions with machine interpretation of the same dataset. Our reasoning is that analysis of any differences and/or correlations between the two could reveal insights into how humans themselves perceive motion and hint at the most important cues that artificial classifiers should be using to perform such a task. We captured biological motion data from human participants engaged in a number of everyday activities, such as walking, running and waving, and then built two artificial classifiers (a Finite State Machine and a multi-layer perceptron artificial neural network, ANN) which were capable of discriminating between activities. We then compared the accuracy of these classifiers with the abilities of a group of human observers to interpret the same activities when they were presented as moving light displays (MLDs). Our results suggest that machine recognition with ANNs is not only comparable to human levels of recognition but can exceed it in some instances.