Improved 2D Human Pose Tracking Using Optical Flow Analysis

Авторы: Khelvas Aleksander, Gilya-Zetinov Alexander, Konyagin Egor, Demyanova Darya, Sorokin Pavel, Khafizov Roman
Сборник: Advances in Intelligent Systems and Computing
Серия: AISC
Том: 1100
Год издания: 2020
Место издания: Springer
Первая страница: 10
Последняя страница: 22
DOI: 10.1007/978-3-030-55187-2_2
Аннотация: In this paper, we propose a novel human body pose refinement method that relies on an existing single-frame pose detector and uses an optical flow algorithm in order to increase quality of output trajectories. First, a pose estimation algorithm such as OpenPose is applied and the error of keypoint position measurement is calculated. Then, the velocity of each keypoint in frame coordinate space is estimated by an optical flow algorithm, and results are merged through a Kalman filter. The resulting trajectories for a set of experimental videos were calculated and evaluated by metrics, which showed a positive impact of optical flow velocity estimations. Our algorithm may be used as a preliminary step to further joint trajectory processing, such as action recognition.
Добавил в систему: Хельвас Александр Валериевич

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