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As research on action recognition matures, the focus is shifting away fromcategorizing basic task-oriented actions using hand-segmented video datasets tounderstanding complex goal-oriented daily human activities in real-worldsettings. Temporally structured models would seem obvious to tackle this set ofproblems, but so far, cases where these models have outperformed simplerunstructured bag-of-word types of models are scarce. With the increasingavailability of large human activity datasets, combined with the development ofnovel feature coding techniques that yield more compact representations, it istime to revisit structured generative approaches.  Here, we describe an end-to-end generative approach from the encoding offeatures to the structural modeling of complex human activities by applyingFisher vectors and temporal models for the analysis of video sequences.  We systematically evaluate the proposed approach on several availabledatasets (ADL, MPIICooking, and Breakfast datasets) using a variety ofperformance metrics. Through extensive system evaluations, we demonstrate thatcombining compact video representations based on Fisher Vectors with HMM-basedmodeling yields very significant gains in accuracy and when properly trainedwith sufficient training samples, structured temporal models outperformunstructured bag-of-word types of models by a large margin on the testedperformance metric.

Cooking in the kitchen: Recognizing and Segmenting Human Activities in Videos