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How Data Pilot improved an IT company’s response time to public safety concerns

Elm

Duration

6 months

Industry

IT Consulting

Services

AI/ML, MLOps

Tools and Technologies

FastAPI
Google Cloud
Angular
CpenCV
FastAPI
Google Cloud
Angular
CpenCV
FastAPI
Google Cloud
Angular
CpenCV
FastAPI
Google Cloud
Angular
CpenCV
FastAPI
Google Cloud
Angular
CpenCV
FastAPI
Google Cloud
Angular
CpenCV
FastAPI
Google Cloud
Angular
CpenCV
FastAPI
Google Cloud
Angular
CpenCV

Company Background

ELM provides innovative secure services & solutions to both public and private sectors in Saudi Arabia. It creates applications and digital products that enable everyone to achieve their aspirations and desires.

It serves over 60,000 private and public organizations, as well as individual customers, helping them achieve remarkable results in efficiency, while also reducing costs and time.

Challenges

Data Pilot dashboard interface showing data analytics and management tools.

Unable to analyze video data

Exclamation triangle – data consulting company Unable to analyze video data The client’s existing system was limited in its ability to integrate video feeds from multiple cameras into a cohesive, scalable solution for the customer.
Data Pilot dashboard interface showing data analytics and management tools.

Delays in data processing

The client’s technical infrastructure lacked the capability to handle high data volumes and varied camera feeds efficiently. These delays led to missed opportunities for resolving security concerns during peak hours or emergency situations.

Solution

Data Pilot developed a comprehensive computer vision-based platform hosted on Google Cloud Platform (GCP). This platform was designed to process live video feeds so that stakeholders can monitor traffic patterns and occupancy levels without any delays, ensuring immediate response to potential issues.

Google Cloud was used to provide the infrastructure for the solution for scalable, reliable processing of video feeds from multiple locations. It also enabled the solution to be scaled to accommodate new features without compromising performance.
A computer vision model was built to enable accurate detection and analysis of footfall, gender, and age, crucial for delivering actionable insights.
The solution was supplemented with a real-time dashboard that displayed live KPIs with customizable reporting, allowing the client to monitor and respond to traffic patterns and crowd behavior instantly.
Data Pilot technology analyzing facial recognition and data in urban environments.

The following key technologies were built to provide this solution:

technologies
Data Pilot dashboard interface showing data analytics and management tools.

Angular

We used Angular for front end development and providing a dynamic and responsive user interface for real-time monitoring.

Data Pilot dashboard interface showing data analytics and management tools.

FastAPI

We used FastAPI for back end and powering the solution’s high-performance processing capabilities.

Data Pilot dashboard interface showing data analytics and management tools.

OpenCV

We used OpenCV for enabling accurate and reliable footfall detection through advanced computer vision.

Data Pilot dashboard interface showing data analytics and management tools.

RTSP Protocol for Video Streams

We used RTSP Protocol for seamlessly ingesting live video streams to enable continuous real-time analysis of live video feeds.

The Impact

Future-proof scalability to accommodate more features

The platform was built with scalability in mind, ensuring that it could accommodate future growth, additional features, and increased data volumes without compromising performance.

The platform’s ability to analyze and display demographic data (such as gender and age) in real-time allowed the client to make more informed decisions, particularly in scenarios requiring precise crowd management.

The reduction in processing delays led to smoother operations, with reduced response times to public safety incidents and a an increase in overall operational efficiency

The dashboard was able to give updates with real-time KPIs without any delays. The KPIs displayed on the dashboard included:

  • Total footfall at a given area
  • Number of persons passing a given area by gender
  • Time spent by persons in each area by gender
  • Age of the person (male or female)
Data Pilot dashboard showing user data analysis and management tools.
Data Pilot dashboard displaying data analytics and visualizations on a laptop screen.

Industry Applications

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