Semi-binary based video features for activity representation

Umakanthan, Sabanadesan, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2013) Semi-binary based video features for activity representation. In de Souza, Paulo, Engelke, Ulrich, & Rahman, Ashfaqur (Eds.) Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, Wrest Point, Hobart, TAS, pp. 178-184.

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Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

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ID Code: 66577
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Detection and representation, Motion analysis, 3D scene understanding, BRISK detector, semi binary based feature detectordescriptor
DOI: 10.1109/DICTA.2013.6691527
ISBN: 9781479921263
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.
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Deposited On: 23 Jan 2014 22:32
Last Modified: 29 Jan 2014 04:56

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