Abstract
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.
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Bogun, I., Ribeiro, E. (2013). Recognizing Human-Object Interactions Using Sparse Subspace Clustering. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_49
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DOI: https://doi.org/10.1007/978-3-642-40261-6_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40260-9
Online ISBN: 978-3-642-40261-6
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