Garuda Drishti
Using Indian Railway's existing CCTV network for crowd management and crime prevention.
Created on 10th September 2023
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Garuda Drishti
Using Indian Railway's existing CCTV network for crowd management and crime prevention.
The problem Garuda Drishti solves
Enhanced Security:
AI and ML enhance security by detecting threats and suspicious behavior, making public spaces safer.
Improved Emergency Response:
The system alerts emergency services swiftly, potentially saving lives during accidents or violence.
Optimized Resource Allocation:
It allocates resources efficiently by tracking congestion and deploying personnel as needed.
Prevent Overcrowding:
AI detects overcrowding and triggers measures to prevent accidents and stampedes.
Traffic Flow Management:
It optimizes traffic flow, reducing congestion and stress for commuters.
Enhanced Event Planning:
Predictive analytics aid in planning logistics and ensuring smooth, safe events.
Data-Driven Decision Making:
Data analysis guides decisions on public safety, resource allocation, and infrastructure improvements.
Cost Reduction:
It reduces costs by preventing incidents and optimizing resource use.
Public Awareness and Accountability:
Transparent use of AI boosts security awareness among the public.
Continuous Improvement:
The system continually adapts, remaining effective against evolving challenges.
Challenges we ran into
Challenge: Privacy Regulations and Ethical Concerns
One significant hurdle was navigating the complex landscape of privacy regulations and ethical considerations associated with monitoring public spaces using AI and ML. As the project involved collecting and analyzing video data from CCTV cameras, ensuring compliance with regulations like GDPR (General Data Protection Regulation) and addressing ethical concerns around surveillance became a top priority.
Solution:
To overcome this challenge, we implemented several strategies:
Anonymization: We employed advanced video anonymization techniques to ensure that personally identifiable information (PII) of individuals captured in the footage was blurred or obfuscated. This helped us comply with privacy regulations while still obtaining valuable crowd behavior insights.
Data Encryption: We ensured that data transmission and storage adhered to robust encryption protocols to protect sensitive information from unauthorized access.
Consent and Transparency: We communicated the purpose and scope of data collection through signage and public awareness campaigns, promoting transparency and obtaining informed consent where required.
Data Retention Policies: We established clear data retention policies, ensuring that video data was stored for only as long as necessary for security and operational purposes, and was promptly deleted when no longer needed.
Regular Audits: We implemented regular audits of our system and data handling processes to verify compliance with privacy regulations and ethical guidelines.
Tracks Applied (1)
Quine Track
Quine
Technologies used