Fighting for a Future Free from Violence: A Framework for Real-Time Detection of 'Signal for Help'

In April 2020, by the start of isolation all around the world to counter the spread of COVID-19, an increase in violence against women and kids has been observed such that it has been named The Shadow Pandemic. To fight against this phenomenon, a Canadian foundation proposed the “Signal for Help” gesture to help people in danger to alert others of being in danger, discreetly. Soon, this gesture became famous among people all around the world, and even after COVID-19 isolation, it has been used in public places to alert them of being in danger and abused. However, the problem is that the signal works if people recognize it and know what it means. To address this challenge, we present a workflow for real-time detection of “Signal for Help” based on two lightweight CNN architectures, dedicated to hand palm detection and hand gesture classification, respectively. Moreover, due to the lack of a “Signal for Help” dataset, we create the first video dataset representing the “Signal for Help” hand gesture for detection and classification applications. While the hand-detection task is based on a pre-trained network, the classifying network is trained using the publicly available Jesture dataset and fine-tuned with the “Signal for Help” dataset through transfer learning. The proposed platform shows an accuracy of91.25% with a video processing capability of 16 fps executed on a machine with Intel [email protected] GHz CPU, 31.2 GB memory, and NVIDIA GeForce RTX 2080 Ti GPU. The high performance and small model size of the proposed approach ensure great suitability for resource-limited devices and embedded applications which has been confirmed by implementing the developed framework on the Jetson Nano Developer Kit and performing a comparison with state-of-the-art hand detection and classification models. The developed platform as well as the created dataset are publicly available.