Gesture Recognition for Emergencies: Dataset and Cross-Condition Analysis

Gestures serve as a powerful medium of nonverbal communication, facilitating the transmission of a wide range of feelings. Hand gestures, in particular, represent a type of nonverbal communication utilized in various areas, including communication among deaf individuals, home automation, and medical applications. This makes gesture recognition an essential focus of research and development in computer vision, robotics, and human-computer interaction (HCI). The primary limitations of the existing research are the limited exploration of Indian ethnicity and the gesture captured in nighttime uncontrolled settings. In response, we have curated a large-scale hand gesture recognition dataset comprising signs frequently used in emergency scenarios. A benchmark study using the proposed dataset has been performed using state-of-the-art hand detection and gesture classification networks through in-the-wild evaluation settings.