Human-Robot Interaction Based on Gestures for Service Robots

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Patrick de Sousa (Author) Other; Tiago Esteves (Author) Other; Daniel Campos (Author) Other; Fábio Duarte (Author) Other; Joana Santos (Author) Other; João Leão (Author) Other; José Xavier (Author) Other; Luís de Matos (Author) Other; Manuel Camarneiro (Author) Other; Marcelo Penas (Author) Other; Maria Miranda (Author) Other; Ricardo Silva (Author) Other; António J. R. Neves (Author) Other; Luís Teixeira (Author) FEUP

Conference proceedings International

Title: European Congress on Computational Methods in Applied Sciences and Engineering: ECCOMAS 2017: VipIMAGE 2017

Pages: 700-709

6th ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE)

Porto, PORTUGAL, OCT 18-20, 2017

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 3 Citations

Other information

Authenticus ID: P-00N-5VX

DOI: 10.1007/978-3-319-68195-5_76

Abstract (EN): Gesture recognition is very important for Human-Robot Interfaces. In this paper, we present a novel depth based method for gesture recognition to improve the interaction of a service robot autonomous shopping cart, mostly used by reduced mobility people. In the proposed solution, the identification of the user is already implemented by the software present on the robot where a bounding box focusing on the user is extracted. Based on the analysis of the depth histogram, the distance from the user to the robot is calculated and the user is segmented using from the background. Then, a region growing algorithm is applied to delete all other objects in the image. We apply again a threshold technique to the original image, to obtain all the objects in front of the user. Intercepting the threshold based segmentation result with the region growing resulting image, we obtain candidate objects to be arms of the user. By applying a labelling algorithm to obtain each object individually, a Principal Component Analysis is computed to each one to obtain its center and orientation. Using that information, we intercept the silhouette of the arm with a line obtaining the upper point of the interception which indicates the hand position. A Kalman filter is then applied to track the hand and based on state machines to describe gestures (Start, Stop, Pause) we perform gesture recognition. We tested the proposed approach in a real case scenario with different users and we obtained an accuracy around 89,7%.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10