WOMEN SAFETY ANALYTICS - PROTECTING WOMEN FROM SAFETY THREATS

Abstract

ABSTRACT: The Women Safety Analytics project addresses the critical need for safety enhancement for women in public places by harnessing the most advanced surveillance technology and data-driven insights. This project uses Al-backed person detection and gender classification to monitor, in real-time, the degree to which gender can be identified across crowds and ·dangerous situations. It detects isolation of a lone woman, generating an alert each time the woman is isolated, monitors hotspots where a woman is surrounded by different men and analyzes proxiinity and behavior to identify potential threats, SOS gestures for immediate notification to emergency services in response to distress signals. It also maps regular safety incidents' hotspots to support community safety planning and resource allocation. With these cutting-edge technologies aligned to the project, Women Safety Analytics wants to build an enabling proactive safety framework for women in public spaces.This project provides critical inputs into the city planning and resource distribution and also engages the community in terms of the detailed mapping of the hotspot and historical data regarding safety incidents. The approach taken through this project is to develop an ·infrastructure that provides a safe space for women to inhabit public spaces. It does so by making a dynamic and responsive surveillance infrastructure that can proactively respond to safety concems.The use of cutting-edge technology is aimed not on.ly at inte":'ention in potentially dangerous scenarios but also at long-tenn safety improvements, since the creation of analytic capacity for local authorities will assist them in their planning and allocation of safety resources to make public spaces safer and more welcoming for women.

Specification

FIELD OF INVENTION:
The field of invention for the project is the monitoring and alarm of threats to women through
security cameras equipped with smart surveillance technology that deals with personal safety
solutions. This project is designed around analytics that use deep learning in real-time threat
detection and bases loT connectivity for instant alerts and communication with authorities through an
interface of immediate reporting. Combining emergency response technology and user-friendly
mobile applications, the project aims to heighten the safety of women by adopting proactive
monitoring systems coupled with rapid notification systems.
BACKGROUND OF INVENTION:
Women Safety Analytics is a response to the rising awareness about safety issues related to women in
public spaces. Indeed, despite all progressive technological and societal advancements, so much
harassment, violence, and intimidation attacks women today. High statistics and alarn1ing cases every
day suggest that time has come to execute practical remedies for women's safety and security.
History has shown that most of the safety solutions were conventionally bound, mainly through
increased police presence or the community watch initiatives. Such solutions could not rely on the
urgency and responsiveness required in critical situations, hence making them inadequate. The new
sman technologies are essentially an opportunity for tapping innovations in surveillance and
analytics for proactive safety solutions.
With rapid advancements in artificial intelligence and machine learning, new avenues are opening for
faster real-time monitoring and threat detection. Processed surveillance by multiple cameras can be
carried out, providing subtly complex analysis of crowd dynamic behavior and individual actions.
Identifying this possibility, .the concept of the Women Safety Analytics project was designed to be
specifically meant for the protection of women using advanced technologies.
The project also considers the aspect of enabling women with skills or tools that enhance personal
safety but also engage communities. The invention seeks to shift this script about women's safety in
public spaces by creating a system that detects threats as much as it facilitates immediate responses
and interventions. This proactive approach should bring about an atmosphere where women feel safe
and supported, ultimately contributing to a more general trend toward greater safety and equality
within society. The total thrust of Women Safety Analytics is thus a reaction to the ongoing issue of
not feeling safe enough for women, using technological capabilities to create an inclusive, responsive
solution both for the individual and the community at large.
OBJECTIVES:
1. Person Detection and Gender Classification:
Accurate system that detects people in the view of the camera. Person identification and classification
by gender in real-time, supported through more complex machine learning algorithms.
2. Gender Distribution:
The demographic composition of monitored areas is monitored through shifts in the gender
distributions, thus providing insights into crowd patterns and potential safety problems.
3. Single Female Identification:
Algorithms will sense if a female is alone in a specific zone; the alert system will alert if she stays
there for a more extended period of time or moves to high-risk areas.
4. Single Female Surrounded by Several Males:
A monitoring system should be developed, which recognizes that the woman is surrounded by
multiple males, proximity, and their behavior, and warn the concemed authorities.
5. SOS Sign/Gesture:
In this feature, identify unique SOS signs or signals that a distress woman has developed that can
trigger alerting immediate services to the emergency services and contacts.
6. Hotspot Identification:
Use data analytics to map out hotspots of increased incidence or emerging potential threats and
inform safety initiatives and proactive measures within urban planning and community engagement.
SUMMARY:
Women Safety Analytics is an effort to provide safety and security to women in public places by
using the advanced surveillance technology coupled with data analytics. The OpenCV-based person
detection and gender classification of this system can accurately detect the presence of individuals
and their gender in real-time. Distribution of gender within crowds is monitored to garner insights
about social dynamics and potential risks.
Key features include lone woman detection, where the authorities are alerted if a woman goes solo,
and identifying situations involving multiple men surrounding one woman with assessment of
proximity and behavior to detect a threat. Another feature is SOS gesture recognition for alerts to
emergency services, when a woman signals distress in an immediate response. It identifies hotspots
where there are frequent safety incidents, informs community safety initiatives and urban planning
efforts, and works towards making the environment safer for women through timely alerts and
actionable data to prevent or respond to possible threats.




CLAIMS:
WE CLAIM,
Claim 1: A real-time gender classification system based on machine learning for the detection and
classification of people's gender even as people move in public places even when conditions change.
Claim 2: The method used to detect the isolated female, which tracks female subjects over a long
period and sends alerts whenever she gets into a predefined high-risk area.
Claim 3: Proximity-based threat detection system that recognizes whether a female is surrounded by
more than one male thus triggering instant alerts.
Claim 4: Gesture recognition system, identified pre-defined SOS or distress gestures that shall
immediately notify emergency contacts and officials with the presence of potential danger.
Claim 5: Location hotspot service that plots areas where safety incidents frequently occur by using
the data analytics of gender distribution to identify possible areas of risk for women.
Claim 6: A behavioral analysis system which identifies proximity and movements of individuals to
alert their presence ncar women in public places.
Claim 7: Automated system for generating real-time alerts to the law enforcement agencies upon the
detection of potential threats towards women.
Claim 8: Video-based monitoring system with gender detection capability; real-time insights on
gender ratios and safety vulnerabilities in public places are communicated.
Claim 9 :An apparatus for time-series gender distribution monitoring and tracking in public spaces
for safe improvement and reduced risk to women.
Claim 10: An SOS hand or body signal recognition chat can automatically dial contact for emergency
services with selected services promptly.

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