ML Video Analytics for Proactive Retail Loss Prevention
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Solution Overview
Problem
Current loss prevention systems in retail locations are reactive and subjective, only detecting loss events after they occur, failing to prevent losses effectively.
Innovation Solution
A system utilizing machine learning classifiers to analyze video feeds from cameras, determining probability certainties of customer activities and applying customizable business rules to identify suspicious behavior in real-time, providing notifications to workers to prevent potential losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional camera systems and tags are used to detect loss events, then loss detection capability is provided, but the system can only detect losses after they occur and cannot prevent them
Solution Approach 1:
The system performs preliminary action by analyzing customer behavior patterns and predicting potential loss events before they occur. Machine learning models process video feeds and sensor data to identify suspicious behaviors early, allowing security personnel to intervene preventively rather than reactively after loss has occurred.
Solution Approach 2:
The system implements continuous feedback loops where loss prevention outcomes are fed back into the machine learning models to improve prediction accuracy over time. Real-time monitoring results and subsequent loss events are used to retrain and refine the predictive algorithms, enhancing the system's ability to anticipate and prevent future losses.
2Reliability
If machine learning classifiers are implemented to analyze video feeds and predict suspicious behavior, then proactive loss prevention capability is improved, but system complexity increases
Solution Approach 1:
The complex loss prevention system is segmented into modular components: video feed processing modules, machine learning classification modules, behavior analysis modules, and notification modules. Each component handles a specific aspect of the analysis pipeline, making the overall system more manageable and easier to maintain while achieving sophisticated predictive capabilities.
Solution Approach 2:
The patent introduces intermediary processing layers between raw video feeds and final loss predictions. Machine learning classifiers act as intermediaries that translate complex video data into simplified probability scores, which are then further processed by business rule engines to generate actionable insights, reducing the complexity burden at any single system level.
3Measurement precision
If multiple machine learning classifiers are used to classify customer activities with probability certainties, then prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by using multiple machine learning classifiers selectively rather than analyzing all possible customer behaviors with equal depth. High-probability suspicious activities are analyzed with multiple classifiers for accuracy, while low-risk activities receive minimal processing, optimizing the balance between prediction accuracy and processing time.
Data Source
AI summary
Examples described herein generally relate to a system for monitoring customers in a retail environment. The system includes a plurality of cameras located in different regions of the retail environment, each camera configured to capture a video feed of a respective region. The system includes a computer system comprising a memory and a processor. The system provides the video feed of at least one region of the retail environment to a plurality of machine learning classifiers, each machine learning classifier trained on labeled videos to classify a sequence of images of a customer into a probability certainty of a respective activity being performed by the customer. The system applies the probability certainties of the respective activities of a customer to a set of business rules to determine whether customer activities identified by the probability certainties indicate suspicious behavior. The system provides a notification of the suspicious behavior to a worker.