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Recognizing Hand-based Actions based on Hip-Joint centered Features using KINECT
Microsoft Kinect provides skeletal joints to extract different features which can be applied to identify different actions performed by subjects. As human moves, skeletal joints contribute to the movements and human actions are nothing but different types of movements in specific orders. Hand wave, hand shaking, push, pull, clapping, throw, catch these are some hand based actions which are difficult to recognize properly in an automated system. Hip-joint plays a vital role to determine joint-based features from human skeleton, as it is approximately the middle joint of the whole skeleton. The joint relative distances (JRD) and joint relative angles (JRA) are used as principle features in recent action recognition methodologies. In this paper, we have proposed a new methodology based on hip-joint centered features which are based on basic physiological movements that contributes to the decent accuracy in identifying hand based actions.
Pattern recognition, Skeleton, Support vector machines, Feature extraction, Image recognition, Decision trees, Three-dimensional displays
Ahmed Al Marouf ; Md. Ferdousur Rahman Sarker ; Shah Md. Tanvir Siddiquee
Journal or Conference Name
2nd International Conference on Electrical and Electronic Engineering, ICEEE 2017
Publish Year