David Paulk
Vangelis Metsis
ABSTRACT
Object recognition serves obvious purposes in assisted living environments, where robotic devices can be used as companions to assist humans in need. The recent introduction of vision based sensors, which are able to extract depth sensing information about the environment, in addition to the traditional RGB video, presents new opportunities and challenges for more accurate object recognition.
The current work, presents an object recognition approach that uses RGB-D point cloud data and a novel feature extraction methodology, in combination with well-known supervised learning algorithms, to achieve accurate, real-time recognition of a large number of objects. In our experiments, we use a dataset of household objects organized into 51 categories, and evaluate the recognition accuracy and time efficiency of a set of different supervised learning methods.
- Belongie, S., Malik, J., and Puzicha, J. Shape matching and object recognition using shape contexts. Pattern Analysis and Machine Intelligence, IEEE Transactions on 24, 4 (2002), 509--522. Google Scholar
Digital Library
- Bo, L., Ren, X., and Fox, D. Unsupervised feature learning for rgb-d based object recognition. In 13th International Symposium on Experimental Robotics (ISER) (2012).Google Scholar
- Bo, L., Ren, X., and Fox, D. Unsupervised feature learning for rgb-d based object recognition. In Experimental Robotics (2013), Springer, pp. 387--402.Google ScholarCross Ref
- Chang, C.-C., and Lin, C.-J. Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2, 3 (2011), 27. Google ScholarDigital Library
- Cignoni, P., Corsini, M., and Ranzuglia, G. Meshlab: an open-source 3d mesh processing system. Ercim news 73 (2008), 45--46.Google Scholar
- Fischler, M. A., and Bolles, R. C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 6 (1981), 381--395. Google ScholarDigital Library
- Khoshelham, K., and Elberink, S. O. Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12, 2 (2012), 1437--1454.Google ScholarCross Ref
- Lai, K., Bo, L., Ren, X., and Fox, D. A large-scale hierarchical multi-view rgb-d object dataset. In IEEE International Conference on Robotics and Automation (ICRA) (2011).Google ScholarCross Ref
- Lai, K., Bo, L., Ren, X., and Fox, D. Detection-based object labeling in 3d scenes. In IEEE International Conference on Robotics and Automation (ICRA) (2012).Google ScholarCross Ref
- Lowe, D. G. Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on (1999), vol. 2, Ieee, pp. 1150--1157. Google ScholarDigital Library
- McMurrough, C., Rich, J., Conly, C., Athitsos, V., and Makedon, F. Multi-modal object of interest detection using eye gaze and rgb-d cameras. In Proceedings of the 4th Workshop on Eye Gaze in Intelligent Human Machine Interaction (2012), ACM, p. 2. Google ScholarDigital Library
- McMurrough, C., Rich, J., Metsis, V., Nguyen, A., and Makedon, F. Low-cost head position tracking for gaze point estimation. In Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) (2012). Google ScholarDigital Library
- McMurrough, C. D., Metsis, V., Rich, J., and Makedon, F. An eye tracking dataset for point of gaze detection. In Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA) (2012). Google ScholarDigital Library
- Russell, S., and Norvig, P. Artificial Intelligence: A Modern Approach. Pearson Education, Inc., 2010. Google ScholarDigital Library
- Rusu, R. B., Marton, Z. C., Blodow, N., Dolha, M., and Beetz, M. Towards 3d point cloud based object maps for household environments. Robotics and Autonomous Systems 56 (2008). Google ScholarDigital Library
- Shapire, R. E., and Freund, Y. Boosting: Foundations and Algorithms. Massachusetts Institute of Technology, 2012. Google ScholarDigital Library
- Shi, L., Kodagoda, S., and Ranasinghe, R. Fast indoor classification using 3d point clouds. In Proceedings of the Australasian Conference on Robotics and Automation (ACRA) (2011).Google Scholar
- Sural, S., Qian, G., and Pramanik, S. Segmentation and histogram generation using the hsv color space for image retrieval. In Image Processing. 2002. Proceedings. 2002 International Conference on (2002), vol. 2, IEEE, pp. II--589.Google Scholar
- Van De Weijer, J., and Schmid, C. Coloring local feature extraction. In Computer Vision--ECCV 2006. Springer, 2006, pp. 334--348. Google ScholarDigital Library
Index Terms
- A supervised learning approach for fast object recognition from RGB-D data
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