In the wake of increasing incidents of violence against women, the ‘Signal for Help’ (SFH) has surfaced as an invaluable silent beacon. Developed as a discrete hand gesture for women under duress to indicate their plight, its importance became even more pronounced during the pandemic when virtual communication surged. The S2CITIES project seeks to amplify the efficacy of this signal through technology.
Using cutting-edge AI-driven hand gesture recognition, S2CITIES continually monitors surveillance footage. Once the SFH is detected, an alert mechanism is activated, instantly notifying the necessary authorities. To augment this system and address potential inaccuracies, a dedicated mobile application was devised. This app serves as a critical tool for law enforcement, allowing swift review and response to these alerts.
Training the AI required a novel approach, given the absence of an existing dataset for the SFH. Through widespread appeals to university students, a unique dataset was amassed.
The implications of S2CITIES are vast. It propels surveillance from passive monitoring to proactive intervention. Preliminary market analysis have pinpointed public transportation sectors and municipalities as primary beneficiaries, given their extensive surveillance infrastructure and the promise of enhanced safety for women.
However, the path to widespread adoption is not without challenges. Concerns around privacy, particularly in the private sector, pose obstacles. Some local businesses fear potential revenue losses, while others grapple with legal implications surrounding surveillance.
In essence, S2CITIES is more than just a tech initiative. It’s a testament to the power of innovation, acting as a bridge between silent pleas for help and tangible, life-saving interventions.