Video Behavior Investigation Generation for Retail Shrinkage Analysis
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Solution Overview
Problem
Existing surveillance systems focus on identifying individual occurrences of theft or violations rather than understanding underlying user behaviors, making it difficult for companies to address the root causes of inventory shrinkage.
Innovation Solution
A system that generates real-time investigations of user behavior using video cameras, video analytics modules, and computers to analyze video and non-video data, identifying specific actions, inactions, movements, and temporal events, and correlates this data with external transaction information to detect potential theft or inefficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional surveillance systems are used to monitor user behavior, then individual occurrences of theft or violations can be identified, but the underlying user behaviors and root causes of inventory shrinkage cannot be understood
Solution Approach 1:
The system segments user behavior into discrete analyzable components by defining specific behaviors (e.g., hand-to-pocket movements, loitering patterns, unusual browsing) and detecting them separately through video analytics, allowing comprehensive understanding of underlying behavioral patterns while maintaining detection precision
Solution Approach 2:
The system transitions from traditional 2D video monitoring to multi-dimensional behavioral analysis by extracting spatial, temporal, and contextual dimensions of user behavior, enabling detection of patterns and root causes that cannot be identified through conventional surveillance methods
2Measurement precision
If manual review of surveillance footage is performed to understand user behavior, then detailed analysis is possible, but time consumption and processing efficiency are significantly reduced
Solution Approach 1:
The system performs preliminary automated analysis of video footage by detecting and flagging suspicious behaviors before human review, pre-processing large volumes of data to identify only relevant segments that require detailed investigation, thereby reducing overall processing time while maintaining analysis accuracy
Solution Approach 2:
The system introduces an automated video analytics module as an intermediary between raw video footage and human investigators, extracting and presenting key behavioral insights that bridge the gap between comprehensive data collection and efficient human review
3Reliability
If comprehensive video monitoring is implemented to detect all user behaviors, then detection capability is improved, but system complexity and computational resources increase
Solution Approach 1:
The system applies different levels of monitoring intensity to different areas and behaviors by defining specific behaviors of interest (e.g., hand-to-pocket movements at POS, loitering in high-value areas) and applying targeted detection algorithms, reducing overall system complexity while maintaining high detection reliability for critical behaviors
Solution Approach 2:
The system dynamically adjusts detection parameters such as sensitivity thresholds, analysis zones, and behavioral definitions based on contextual factors like time of day, location, and transaction types, enabling reliable detection across varying conditions without requiring uniformly complex system architecture
Data Source
AI summary
The present disclosure is directed to systems and methods for generating investigations of user behavior. In an example embodiment, the system includes a video camera configured to capture video of user activity, a video analytic module to perform real-time video processing of the captured video to generate non-video data from video, and a computer configured to receive the video and the non-video data from the video camera. The computer includes a video analytics module configured to analyze one of video and non-video data to identify occurrences of particular user behavior, and an investigation generation module configured to generate an investigation containing at least one video sequence of the particular user behavior. In some embodiments, the investigation is generated in near real time. The particular user behavior may be defined as an action, an inaction, a movement, a plurality of event occurrences, a temporal event and/or an externally-generated event.