A Review on Vision-based Hand Gesture Recognition Targeting RGB-Depth Sensors

Prashant Rawat, Lalit Kane, Mrinal Goswami, Avani Jindal and Shriya Sehgal
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Prashant Rawat: Department of Computer Science, Systemics Cluster, University of Petroleum and Energy Stuides, Dehradun, India
Lalit Kane: Department of Computer Science, Systemics Cluster, University of Petroleum and Energy Stuides, Dehradun, India
Mrinal Goswami: Department of Computer Science, Systemics Cluster, University of Petroleum and Energy Stuides, Dehradun, India
Avani Jindal: Department of Computer Science, Systemics Cluster, University of Petroleum and Energy Stuides, Dehradun, India
Shriya Sehgal: Department of Computer Science, Systemics Cluster, University of Petroleum and Energy Stuides, Dehradun, India

International Journal of Information Technology & Decision Making (IJITDM), 2023, vol. 22, issue 01, 115-156

Abstract: With the advancement of automation, vision-based hand gesture recognition (HGR) is gaining popularity due to its numerous uses and ability to easily communicate with machines. However, identifying hand positions is the most difficult assignment due to the fact of crowded backgrounds, sensitivity to light, form, speed, size, and self-occlusion. This review summarizes the most recent studies on hand postures and motion tracking using a vision-based approach by applying Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The parts and subsections of this review article are organized into numerous categories, the most essential of which are picture acquisition, preprocessing, tracking and segmentation, feature extraction, collation of key gesture identification phases, and classification. At each level, the various algorithms are evaluated based on critical key points such as localization, largest blob, per pixel binary segmentation, depth information, and so on. Furthermore, the datasets and future scopes of HGR approaches are discussed considering merits, limitations, and challenges.

Keywords: Hand gesture recognition; spatio-temporal features; depth sequence; human–computer interaction (HCI) (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219622022300026

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