Use Case
Due to COVID-19, more people are picking up merchandise from stores rather than in-person shopping.
The surge in pickup shopping has led to increased traffic in store parking lots. Increased customer vehicle traffic is competing with commercial delivery vehicles and causing congestion.
In response to this uptick, Retailer needs to better understand the traffic patterns to implement capacity planning measures.
The Retailer currently has multiple cameras in their store parking lots at ingress/egress for reading license plates (LPR). Dragonfruit collects video from these camera sources and uses the data — paired with real-time AI — to conduct a daily count of vehicles coming into the store’s parking lots.
Using Dragonfruit’s AI analytics, we break up this video data to filter vehicles into categories — cars, trucks, etc. — to help understand how many and what types of vehicles are going through the store’s parking lots.
At the end of the day, Dragonfruit compiles a daily report of the vehicle traffic in the Retailer's parking lots. Dragonfruit AI technology is used to count the types of vehicles on an hourly basis so that the Retailer can better understand traffic patterns, anticipate surges, and implement workarounds such as temporary parking spots for customers, etc.
Dragonfruit overlays on existing infrastructure to add new AI functionality so there is no need to buy new cameras or hardware.
Flexible, pay-as-you-go pricing and ability to easily turn on/off features speeds up proof-of-concept trials and allows Retailers to quickly get to full deployment.
Retailers can implement and manage hundreds of camera sources without a huge outlay of budget.
