Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting

Sun, L., Aragon-Camarasa, G. , Rogers, S. , Stolkin, R. and Siebert, J. P. (2017) Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, British Columbia, Canada, 24-28 Sept 2017, pp. 6699-6706. ISBN 9781538626825 (doi:10.1109/IROS.2017.8206586)

[img]
Preview
Text
146761.pdf - Accepted Version

3MB

Abstract

This paper proposes a single-shot approach for recognising clothing categories from 2.5D features. We propose two visual features, BSP (B-Spline Patch) and TSD (Topology Spatial Distances) for this task. The local BSP features are encoded by LLC (Locality-constrained Linear Coding) and fused with three different global features. Our visual feature is robust to deformable shapes and our approach is able to recognise the category of unknown clothing in unconstrained and random configurations. We integrated the category recognition pipeline with a stereo vision system, clothing instance detection, and dual-arm manipulators to achieve an autonomous sorting system. To verify the performance of our proposed method, we build a high-resolution RGBD clothing dataset of 50 clothing items of 5 categories sampled in random configurations (a total of 2,100 clothing samples). Experimental results show that our approach is able to reach 83.2% accuracy while classifying clothing items which were previously unseen during training. This advances beyond the previous state-of-the-art by 36.2%. Finally, we evaluate the proposed approach in an autonomous robot sorting system, in which the robot recognises a clothing item from an unconstrained pile, grasps it, and sorts it into a box according to its category. Our proposed sorting system achieves reasonable sorting success rates with single-shot perception.

Item Type:Conference Proceedings
Additional Information:This work was supported by: European FP7 Strategic Research Project CloPeMa, www.clopema.eu; H2020 RoMaNs, 645582, www.h2020romans.eu; EPSRC grant EP/M026477/1.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Sun, Li and Aragon Camarasa, Dr Gerardo and Siebert, Dr Paul and Rogers, Dr Simon
Authors: Sun, L., Aragon-Camarasa, G., Rogers, S., Stolkin, R., and Siebert, J. P.
College/School:College of Science and Engineering > School of Computing Science
ISSN:2153-0866
ISBN:9781538626825
Published Online:14 December 2017
Copyright Holders:Copyright © 2017 IEEE
First Published:First published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017): 6699-6706
Publisher Policy:Reproduced in accordance with the publisher copyright policy
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record