Non-trajectory-based gesture recognition in human-computer interaction based on hand skeleton data

Jia, Lesong; Xue, Chengqi

doi:10.1007/s11042-022-12355-8

Cited by 6 publications

(1 citation statement)

References 43 publications

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“…Hidden Markov Models are famous for modeling and recognizing human behaviour [17]. HMMs are statistical models that can represent complex temporal patterns and have been effectively used in human activity recognition and motion analysis [18]. HMMs were employed to recognize tennis actions from marker-based motion capture data [19,20].…”

Section: Markers-based Hirmentioning

confidence: 99%

A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

Bukht,

Mudawi,

Alotaibi

et al. 2023

CMC

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Human-human interaction recognition is crucial in computer vision fields like surveillance, human-computer interaction, and social robotics. It enhances systems' ability to interpret and respond to human behavior precisely. This research focuses on recognizing human interaction behaviors using a static image, which is challenging due to the complexity of diverse actions. The overall purpose of this study is to develop a robust and accurate system for human interaction recognition. This research presents a novel image-based human interaction recognition method using a Hidden Markov Model (HMM). The technique employs hue, saturation, and intensity (HSI) color transformation to enhance colors in video frames, making them more vibrant and visually appealing, especially in low-contrast or washed-out scenes. Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method. Feature extraction uses the features from Accelerated Segment Test (FAST), Oriented FAST, and Rotated BRIEF (ORB) techniques. The application of Quadratic Discriminant Analysis (QDA) for feature fusion and discrimination enables high-dimensional data to be effectively analyzed, thus further enhancing the classification process. It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities. The impressive accuracy rates of 93% and 94.6% achieved in the BIT-Interaction and UT-Interaction datasets respectively, highlight the success and reliability of the proposed technique. The proposed approach addresses challenges in various domains by focusing on frame improvement, silhouette and feature extraction, feature fusion, and HMM classification. This enhances data quality, accuracy, adaptability, reliability, and reduction of errors.

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