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
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 this solution to map each client’s needs and solve even the most demanding requests.
The rinf.tech proposed architecture is the following:
Furthermore, we employed:
The main features include: people counting, gender and age breakdown, in depth visitor analysis, interval comparison & selection, direction and heatmap analysis, timeline comparison, queue time and waiting number of people.
Rinf.tech assembled a team of technology experts to understand the problem areas to be addressed to derive proper analytics.
Based on the requirements & solutions discussed, mockups with single process flows were created. The mockups helped the client in visualizing the system even before it was built. This helped those who were unaware of the workings of IT systems to give feedback & suggestions for further improvement.
The retail analytics application helps our client: