Recognition of Marker-less Human Actions in Videos Using Hidden Markov Models
نویسندگان
چکیده
In this paper, we present a novel methodology for using Hidden Markov Models (HMM) to recognize marker-less human actions in videos. We use Scale Invariant Feature Transform (SIFT) keypoints to identify and track the trajectories of objects and major body parts in instructional videos. Then, we use a HMM to recognize the trajectories as a human action. This enables the use of readily available instructional videos from the Internet to train robots to perform tasks instead of programming them. The HMM we designed is able to recognize a marker-less human grasping an object in a video with a precision of 80%. The experiments also showed that the HMM can predict whether the human is reaching, is grasping, or has grasped the object with an accuracy of 75%.
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