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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-6
A. Oikonomopoulos , Computing Department Imperial College London, UK
M. Pantic , Computing Department Imperial College London, UK
I. Patras , Electronic Engineering Department, Queen Mary University of London, UK
ABSTRACT
The extraction and quantization of local image and video descriptors for the subsequent creation of visual codebooks is a technique that has proved extremely effective for image and video retrieval applications. In this paper we build on this concept and extract a new set of visual descriptors that are derived from spatiotemporal salient points detected on given image sequences and provide local space-time description of the visual activity. The proposed descriptors are based on the geometrical properties of three-dimensional piecewise polynomials, namely B-splines, that are fitted on the spatiotemporal locations of the salient points that are engulfed within a given spatiotemporal neighborhood. Our descriptors are inherently translation invariant, while the use of the scales of the salient points for the definition of the neighborhood dimensions ensures space-time scaling invariance. Subsequently, a clustering algorithm is used in order to cluster our descriptors across the whole dataset and create a codebook of visual verbs, where each verb corresponds to a cluster center. We use the resulting codebook in a ‘bag of verbs’ approach in order to recover the pose and short-term motion of subjects at a short set of successive frames, and we use Dynamic Time Warping (DTW) in order to align the sequences in our dataset and structure in time the recovered poses. We define a kernel based on the similarity measure provided by the DTW to classify our examples in a Relevane Vector Machine classification scheme. We present results in a well established human
CITATION
A. Oikonomopoulos, M. Pantic, I. Patras, "B-spline polynomial descriptors for human activity recognition", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-6, doi:10.1109/CVPRW.2008.4563175
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