A light-weight real-time applicable hand gesture recognition system for automotive applications

Thomas Kopinski 1 Stéphane Magand 2 Alexander Gepperth 3, 2 Uwe Handmann 1
1 HRW - Hochschule Ruhr West
2 RV - Robotique et Vision
3 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : We present a novel approach for improved hand-gesture recognition by a single time-of-flight(ToF) sensor in an automotive environment. As the sensor's lateral resolution is comparatively low, we employ a learning approach comprising multiple processing steps, including PCA-based cropping, the computation of robust point cloud descriptors and training of a Multilayer perceptron (MLP) on a large database of samples. A sophisticated temporal fusion technique boosts the overall robustness of recognition by taking into account data coming from previous classification steps. Overall results are very satisfactory when evaluated on a large benchmark set of ten different hand poses, especially when it comes to generalization on previously unknown persons.
Document type :
Conference papers
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download
https://hal-ensta-paris.archives-ouvertes.fr//hal-01251413
Contributor : Alexander Gepperth <>
Submitted on : Wednesday, January 6, 2016 - 2:36:15 PM
Last modification on : Wednesday, July 3, 2019 - 10:48:05 AM
Long-term archiving on : Thursday, April 7, 2016 - 3:54:28 PM

File

root.pdf
Files produced by the author(s)

Identifiers

Collections

ENSTA | INRIA-BORDEAUX | INRIA | ENSTA_U2IS | UNIV-PARIS-SACLAY

Citation

Thomas Kopinski, Stéphane Magand, Alexander Gepperth, Uwe Handmann. A light-weight real-time applicable hand gesture recognition system for automotive applications. IEEE International Symposium on Intelligent Vehicles (IV), Jun 2015, Seoul, South Korea. pp.336-342, ⟨10.1109/IVS.2015.7225708⟩. ⟨hal-01251413⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

407

Files downloads

628

Contact

CCSD