Sensor substitution for video-based action recognition

Rupprecht C, Lea C, Tombari F, Navab N, Hager GD (2016)

Publication Type: Conference contribution

Publication year: 2016

Journal

IEEE/RSJ International Conference on Intelligent Robots and Systems IEEE

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2016-November

Pages Range: 5230-5237

Conference Proceedings Title: IEEE International Conference on Intelligent Robots and Systems

Event location: Daejeon, KOR

ISBN: 9781509037629

DOI: 10.1109/IROS.2016.7759769

Abstract

There are many applications where domainspecific sensing, such as accelerometers, kinematics, or force sensing, provide unique and important information for control or for analysis of motion. However, it is not always the case that these sensors can be deployed or accessed beyond laboratory environments. For example, it is possible to instrument humans or robots to measure motion in the laboratory in ways that it is not possible to replicate in the wild. An alternative, which we explore in this paper, is to address situations where accurate sensing is available while training an algorithm, but for which only video is available for deployment. We present two examples of this sensory substitution methodology. The first variation trains a convolutional neural network to regress real-valued signals, including robot end-effector pose, from video. The second example regresses binary signals derived from accelerometer data which signifies when specific objects are in motion. We evaluate these on the JIGSAWS dataset for robotic surgery training assessment and the 50 Salads dataset for modeling complex structured cooking tasks. We evaluate the trained models for video-based action recognition and show that the trained models provide information that is comparable to the sensory signals they replace.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Johns Hopkins University (JHU) US United States (USA) (US)

How to cite

APA:

Rupprecht, C., Lea, C., Tombari, F., Navab, N., & Hager, G.D. (2016). Sensor substitution for video-based action recognition. In IEEE International Conference on Intelligent Robots and Systems (pp. 5230-5237). Daejeon, KOR: Institute of Electrical and Electronics Engineers Inc..

MLA:

Rupprecht, Christian, et al. "Sensor substitution for video-based action recognition." Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, KOR Institute of Electrical and Electronics Engineers Inc., 2016. 5230-5237.

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