Abstract—This paper presents an approach to recognizing people’s actions directed towards a robot, from a video collected by a camera positioned on the robot. Our approach has two modes: “stand by” mode – when a system waits for an action to start and “active” mode – when the system recognizes actions from the video stream. “Stand by” mode allows to save resources during an absence of actions and also gives a starting point of an action, which helps for recognition. Our action recognition model is based on a Bag-of-Words model, but we utilized sparse features in order to process video frames faster. As a result, the system implemented with this approach could continuously recognize actions in real-time using fewer computations. The performance of our method was experimentally evaluated and the results are given at the end of the paper.
Index Terms—Human action recognition, real-time video processing, bag-of-words, computer vision.
M. Maximov is with the Interaction & Robotics Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea and also with Korea University of Science and Technology, Republic of Korea (e-mail: maxsqr@gmail.com).
S. R. Oh is with Korea Institute of Science and Technology, Seoul, Republic of Korea (e-mail: sroh@kist.re.kr).
M. S. Park is with the Interaction & Robotics Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea (e-mail: meister1@gmail.com).
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Cite:Maxim Maximov, Sang-Rok Oh, and Myong Soo Park, "Real-Time Action Recognition System from a First-Person View Video Stream," International Journal of Computer Theory and Engineering vol. 9, no. 2, pp. 79-86, 2017.
