StopLift Checkout Vision Systems With roots from MIT and Harvard, Boston-based StopLift develops software-based checkout vision systems which automatically analyze regular CCTV video from existing cameras to detect various forms of theft, training error, and operational analytics at the checkout.

A pioneer in the field of checkout vision systems, StopLift has developed the first ever system capable of successfully detecting “sweethearting” between cashiers and customers.

Rather than taking a one-size-fits-all approach, StopLift develops targeted applications to address the specific needs of retailers from different sectors including general merchandise, grocery, and specialty retail.

Roots in Research

StopLift grew out of a Harvard Business School field study on Retail Loss Prevention entitled "Project StopLift".  One of the major findings of the study was that while CCTV is the most widespread of all loss prevention technologies, it is often the most underutilized - it is just too expensive and time-consuming to monitor or review video comprehensively by humans. 

With engineering talent and computer vision research insights from MIT, the Project StopLift team realized that video recognition could be used to automate and thus make possible the comprehensive examination of surveillance video.