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This paper presents a survey of human action recognition approaches based onvisual data recorded from a single video camera. We propose an organizingframework which puts in evidence the evolution of the area, with techniquesmoving from heavily constrained motion capture scenarios towards morechallenging, realistic, "in the wild" videos. The proposed organization isbased on the representation used as input for the recognition task, emphasizingthe hypothesis assumed and thus, the constraints imposed on the type of videothat each technique is able to address. Expliciting the hypothesis andconstraints makes the framework particularly useful to select a method, givenan application. Another advantage of the proposed organization is that itallows categorizing newest approaches seamlessly with traditional ones, whileproviding an insightful perspective of the evolution of the action recognitiontask up to now. That perspective is the basis for the discussion in the end ofthe paper, where we also present the main open issues in the area.

Action Recognition in Videos: from Motion Capture Labs to the Web