Recognising complex activities with histograms of relative tracklets

Stein, S. and McKenna, S. J. (2017) Recognising complex activities with histograms of relative tracklets. Computer Vision and Image Understanding, 154, pp. 82-93. (doi: 10.1016/j.cviu.2016.08.012)

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One approach to the recognition of complex human activities is to use feature descriptors that encode visual inter-actions by describing properties of local visual features with respect to trajectories of tracked objects. We explore an example of such an approach in which dense tracklets are described relative to multiple reference trajectories, providing a rich representation of complex interactions between objects of which only a subset can be tracked. Specifically, we report experiments in which reference trajectories are provided by tracking inertial sensors in a food preparation sce-nario. Additionally, we provide baseline results for HOG, HOF and MBH, and combine these features with others for multi-modal recognition. The proposed histograms of relative tracklets (RETLETS) showed better activity recognition performance than dense tracklets, HOG, HOF, MBH, or their combination. Our comparative evaluation of features from accelerometers and video highlighted a performance gap between visual and accelerometer-based motion features and showed a substantial performance gain when combining features from these sensor modalities. A considerable further performance gain was observed in combination with RETLETS and reference tracklet features.

Item Type:Articles
Additional Information:This research was funded by RCUK grants EP/G066019/1 and EP/K037293/1.
Glasgow Author(s) Enlighten ID:Stein, Dr Sebastian
Authors: Stein, S., and McKenna, S. J.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Computer Vision and Image Understanding
ISSN (Online):1090-235X
Published Online:01 September 2016
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in Computer Vision and Image Understanding 154: 82-93
Publisher Policy:Reproduced under a Creative Commons License
Data DOI:10.15132/10000120

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