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Sports Broadcasting with Deep Learning
http://doi.org/10.5626/JOK.2019.46.10.1020
Sports broadcasting requires understanding and reasoning of a current situation based on information regarding sports scenes, players, and past knowledge. In this paper, we introduced how scene classifier, player detector, motion recognizer could be used to obtain information on sports images and understand current situations. We created three types of commentaries. One was from web data, another was from 13 scenes with scene classifier, and the other was generated by the position of the players, eight motions, and the ontology. Data from the KBO (Korea Baseball Organization League) games from April 1, 2018, to April 14, 2018, were directly labeled to learn the model.
Image Based Human Action Recognition System to Support the Blind
ByoungChul Ko, Mincheol Hwang, Jae-Yeal Nam
In this paper we develop a novel human action recognition system based on communication between an ear-mounted Bluetooth camera and an action recognition server to aid scene recognition for the blind. First, if the blind capture an image of a specific location using the ear-mounted camera, the captured image is transmitted to the recognition server using a smartphone that is synchronized with the camera. The recognition server sequentially performs human detection, object detection and action recognition by analyzing human poses . The recognized action information is retransmitted to the smartphone and the user can hear the action information through the text-to-speech (TTS). Experimental results using the proposed system showed a 60.7% action recognition performance on the test data captured in indoor and outdoor environments.
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