Modeling and visual recognition of human actions and interactions
Résumé
This work addresses the problem of recognizing actions and interactions in realistic video settings such as movies and consumer videos. The first contribution of this thesis (Chapters 2 and 4) is concerned with new video representations for action recognition. We introduce local space-time descriptors and demonstrate their potential to classify and localize actions in complex settings while circumventing the difficult intermediate steps of person detection, tracking and human pose estimation. The material on bag-of-features action recognition in Chapter 2 is based on publications [L14, L22, L23] and is related to other work by the author [L6, L7, L8, L11, L12, L13, L16, L21]. The work on object and action localization in Chapter 4 is based on [L9, L10, L13, L15] and relates to [L1, L17, L19, L20]. The second contribution of this thesis is concerned with weakly-supervised action learning. Chap- ter 3 introduces methods for automatic annotation of action samples in video using readily-available video scripts. It addresses the ambiguity of action expressions in text and the uncertainty of tem- poral action localization provided by scripts. The material presented in Chapter 3 is based on publications [L4, L14, L18]. Finally Chapter 5 addresses interactions of people with objects and concerns modeling and recognition of object function. We exploit relations between objects and co-occurring human poses and demonstrate object recognition improvements using automatic pose estimation in challenging videos from YouTube. This part of the thesis is based on the publica- tion [L2] and relates to other work by the author [L3, L5].
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