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arXiv · 1502.03851

Discovering Human Interactions in Videos with Limited Data Labeling

Mehran Khodabandeh·Arash Vahdat·Guang-Tong Zhou·Hossein Hajimirsadeghi·Mehrsan Javan Roshtkhari·Greg Mori·Stephen Se
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Abstract

We present a novel approach for discovering human interactions in videos. Activity understanding techniques usually require a large number of labeled examples, which are not available in many practical cases. Here, we focus on recovering semantically meaningful clusters of human-human and human-object interaction in an unsupervised fashion. A new iterative solution is introduced based on Maximum Margin Clustering (MMC), which also accepts user feedback to refine clusters. This is achieved by formulating the whole process as a unified constrained latent max-margin clustering problem. Extensive experiments have been carried out over three challenging datasets, Collective Activity, VIRAT, and UT-interaction. Empirical results demonstrate that the proposed algorithm can efficiently discover perfect semantic clusters of human interactions with only a small amount of labeling effort.

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Mehran Khodabandeh, Arash Vahdat, Guang-Tong Zhou, Hossein Hajimirsadeghi, Mehrsan Javan Roshtkhari, Greg Mori, Stephen Se. 2015-02-12. Discovering Human Interactions in Videos with Limited Data Labeling. https://arxiv.org/abs/1502.03851

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