6533b7d3fe1ef96bd12614c7
RESEARCH PRODUCT
Pose classification using support vector machines
Edoardo ArdizzoneAntonio ChellaRoberto Pirronesubject
Artificial neural networkCovariance matrixbusiness.industryComputer scienceBinary imagePattern recognitionMobile robotSilhouetteSupport vector machineOperator (computer programming)Gesture recognitionComputer visionArtificial intelligencebusinessEigenvalues and eigenvectorsdescription
In this work a software architecture is presented for the automatic recognition of human arm poses. Our research has been carried on in the robotics framework. A mobile robot that has to find its path to the goal in a partially structured environment can be trained by a human operator to follow particular routes in order to perform its task quickly. The system is able to recognize and classify some different poses of the operator's arms as direction commands like "turn-left", "turn-right", "go-straight", and so on. A binary image of the operator silhouette is obtained from the gray-level input. Next, a slice centered on the silhouette itself is processed in order to compute the eigenvalues vector of the pixels covariance matrix. Finally, a support vector machine is trained to classify different poses using the eigenvalues array. A detailed description of the system is presented. Experimental results and an outline of the usability of the system as a generic shape classification tool are reported.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2000-01-01 | Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium |