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We describe a method for using crowd-sourced labor to track motion and ultimately annotate gestures of humans in video. Our chosen platform for deployment, Amazon Mechanical Turk, divides labor into HITs (Human Intelligence Tasks). Given the informational density of video, our task is potentially larger than a traditional HIT that involves processing a block of text or a single image. We exploit redundancies in video data in such a way that workers' efforts can be multiplied in effect. In the end, a fraction of frames need to be annotated by hand, but we can still achieve complete coverage of all video frames. This is achieved with a combination of HITs using a novel user interface, combined with automatic techniques such as template tracking and affinity propagation clustering. We show in a case study how we can annotate a video database of political speeches with 2D positions and 3D hand pose configurations. This data is then used for some preliminary analytical tasks.

This paper shows how "Body Motion Signature Analysis" -a new "soft-biometrics" technique -can be used for identity verification. It is able to extract motion features from the upper body of people and estimates so called "super-features" for input to a classifier. We demonstrate how this new technique can be used to identify people just based on their motion, or it can be used to significantly improve "hard-biometrics" techniques. For example, face verification achieves on this domain 6.45% Equal Error Rate (EER), and the combined verification performance of motion features and face reduces the error to 4.96% using an adaptive score-level integration method. The more ambiguous motion-only performance is 17.1% EER.

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