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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Recognizing Human-Object Interactions Using Sparse Subspace Clustering
verfasst von : Ivan Bogun, Eraldo Ribeiro
Erschienen in: Computer Analysis of Images and Patterns
Verlag: Springer Berlin Heidelberg
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In this paper, we approach the problem of recognizing human-object interactions from video data. Using only motion trajectories as input, we propose an unsupervised framework for clustering and classifying videos of people interacting with objects. Our method is based on the concept of sparse subspace clustering, which has been recently applied to motion segmentation. Here, we show that human-object interactions can be seen as trajectories lying on a low-dimensional subspace, and which can in turn be recovered by subspace clustering. Experimental results, performed on a publicly available dataset, show that our approach is comparable to the state-of-the-art.