Behera, A, Hogg, DC and Cohn, AG (2013) Egocentric activity monitoring and recovery. In: Lee, KM, Matsushita, Y, Rehg, JM and Hu, Z, (eds.) Computer Vision - ACCV 2012, 11th Asian Conference on Computer Vision, Revised Selected Papers, Part III. 11th Asian Conference on Computer Vision, 05-09 Nov 2012, Daejeon, Korea. Springer , 519 - 532. ISBN 978-3-642-37431-9
Abstract
This paper presents a novel approach for real-time egocentric activity recognition in which component atomic events are characterised in terms of binary relationships between parts of the body and manipulated objects. The key contribution is to summarise, within a histogram, the relationships that hold over a fixed time interval. This histogram is then classified into one of a number of atomic events. The relationships encode both the types of body parts and objects involved (e.g. wrist, hammer) together with a quantised representation of their distance apart and the normalised rate of change in this distance. The quantisation and classifier are both configured in a prior learning phase from training data. An activity is represented by a Markov model over atomic events. We show the application of the method in the prediction of the next atomic event within a manual procedure (e.g. assembling a simple device) and the detection of deviations from an expected procedure. This could be used for example in training operators in the use or servicing of a piece of equipment, or the assembly of a device from components. We evaluate our approach (’Bag-of-Relations’) on two datasets: ‘labelling and packaging bottles’ and ‘hammering nails and driving screws’, and show superior performance to existing Bag-of-Features methods that work with histograms derived from image features [1]. Finally, we show that the combination of data from vision and inertial (IMU) sensors outperforms either modality alone.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2013, Springer. This is an author produced version of a paper published in Computer Vision -- ACCV 2012. Uploaded in accordance with the publisher's self-archiving policy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Nov 2014 14:26 |
Last Modified: | 19 Dec 2022 13:28 |
Published Version: | http://link.springer.com/chapter/10.1007/978-3-642... |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-642-37431-9_40 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:81161 |