Fast Unsupervised Ego-Action Learning for First-person Sports Videos

Portable high-quality sports cameras (e.g. head or helmet mounted) built for recording dynamic first-person video footage are becoming a common item among many sports enthusiasts. We address the novel task of discovering firstperson action categories (which we call ego-actions) which can be useful for such tasks as video indexing and retrieval. In order to learn ego-action categories, we investigate the use of motion-based histograms and unsupervised learning algorithms to quickly cluster video content. Our approach assumes a completely unsupervised scenario, where labeled training videos are not available, videos are not pre-segmented and the number of ego-action categories are unknown. In our proposed framework we show that a stacked Dirichlet process mixture model can be used to automatically learn a motion histogram codebook and the set of ego-action categories. We quantitatively evaluate our approach on both in-house and public YouTube videos and demonstrate robust ego-action c...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Kris Kitani, Yoichi Sato, Takahiro Okabe, Akihiro Sugimoto
• Keywords: Computer Vision, CVPR 2011, Ego-action Categories, Video, firstperson Action Categories