Cognitive Video Analysis
Using the latest state-of-the-art Cognitive Video Analysis the CaddyCheck® suite of products are guaranteed to outperform all competition.
Cognitive Artificial Intelligence
The engine behind CaddyCheck® is based on Cognitive Artificial Intelligence, a deep learning proprietary algorithm that analyses data in real time to identify and report exceptions to the expected behaviour of any object. Exception notifications can be communicated locally or remotely depending on client operational requirements.
This Cognitive Artificial Intelligence engine is state of the art and goes far beyond the capacity of any retail solution currently available for real-time date analysis.
Cognitive Video Analysis
Cognitive Video Analysis is the result of combining Cognitive Vision (the integration of cognitive abilities into computer vision methods) and Artificial Profiling (a software tool capable of identifying and categorising different objects using a cognitive process much like people do).
The use and application of Cognitive Video Analysis to resolve a broad array of retail, industry and societal challenges, is limited only by the imagination
Cognitive Vision integrates cognitive capabilities into computer vision methods, creating state of the art algorithms that deliver Artificial Intelligence.
This AI is capable of making assumptions, creating hypotheses, remembering, learning from experience, weighing alternative solutions and developing new strategies that mimic human cognitive behaviour.
CaddyCheck® technology integrates deep learning algorithms with other machine learning approaches to deliver a system capable of making autonomous decisions, improving results based on real experience.
As a result, our cognitive algorithms bring a new level of resiliency, robustness and responsiveness to changes in the local environment.
CaddyCheck® uses a complex Artificial Profiler (AP). This software tool is capable of identifying and categorising different objects using a cognitive process much like people do.
Once it´s taught the basics, the AP is capable of identifying objects separately. The more information received by the Profiler, the faster it learns, and the more accurate it becomes.
Employing this same process, CaddyCheck® identifies empty shopping carts and ignores them, whilst recognising those carts that have articles in or under them, notifying the employee accordingly, and recording the event for comparative analysis.
The CaddyCheck® algorithm resolves these scenarios with a high degree of accuracy, employing an innovative and comprehensive approach, in addition to the built-in capability for self-learning and improvement