Single and two-person(s) pose estimation based on R-WAA

Human pose estimation methods have difficulties predicting the correct pose for persons due to challenges in scale variation. Existing works in this domain mainly focus on single-person pose estimation. To counter this challenge we have developed a system that can efficiently estimate both one and two individual poses. We termed remarkable joint based, Waveform, Angle, and Alpha characteristics, as R-WAA. R-WAA is a novel up-bottom human pose estimation method developed using two-dimensional body skeletal joint points. They are capturing all required spatial information using waveform characteristics, angle characteristics, and alpha characteristics. All pose estimator characteristics are developed using a remarkable joint, which is the origin of all poses. The proposed algorithm is evaluated for one and two individuals databases: KARD- Kinect Activity Recognition Dataset and SBU Kinect Interaction Dataset. The results of experiments validate that R-WAA outperforms state-of-the-art approaches.

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