CCTV Analytics

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Client: European retail business

Technology

Facial Recognition, Queue Detector, People Counter, Activity Visualizer and Analytics

PostgreSQL, ASP.NET Web Services, Web API, IIS service for hosting the Web Application and Web API, Windows service for data synchronization Web Application, React JS

Services

Dashboard development

Challenges

High inefficiency in defining customer clusters by gender and age, resulting in slow follow-ups across departments and locations due to variability in operations.
Inability to implement immediate response programs.
Lack of understanding of customer behavior.
Multiple delays in feature approval leading to loss of potential business opportunities.

Solution

Using stream cameras and video analytics servers (which include Facial Recognition, Queue Detector, People Counter, Activity Visualizer and Analytics system package for retail), rinf.tech has developed a high-tech solution to map each client’s needs and solve even the most demanding requests.

Proposed Architecture

PostgreSQL – fully managed database
ASP.NET Web Services and Web API
IIS service for hosting the Web Application and Web API

Employed

Windows service for data synchronization
Web Application (React JS frontend ) to store, view and compare customer data against various criteria.
Third party software integration for face recognition, queue & people count data analysis.
Third party cameras with face recognition, people count & heatmap processing functionalities.

Main Features

people counter
gender and age breakdown
in depth visitor analytics
interval comparison and selection, direction & heatmap analysis
timeline comparison, queue time & number of people waiting
cameras with people counting capabilities,
face recognition systems
third party systems with push notifications events

Methodology

Problem definition

Rinf.tech assembled a team of technology experts to understand the problem areas to be addressed with the custom solution.

Testing the focus group

Based on the requirements & solutions discussed, mockups with single process flows were created. The mockups helped the client visualize the system before it was built. This helped collect feedback for further improvement.

Designing the Solution

The solution architect created and introduced a configurable dashboard based on defined rules and conditions. This dashboard is scalable to meet the client’s ever-evolving reporting needs.

Application Launch

After the client’s sign-off on the dashboard, the analytics solutions were launched and tested for effectiveness and efficiency at client sites with a pilot run in a supervised group of participants.

Results

90% increase in customer insights
50% increase of the immediate response rate
60% increase in accuracy of customer patterns by using direction and heatmap analysis