Video Analytics for User Behavior Investigation
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Solution Overview
Problem
Existing video observation and surveillance systems primarily focus on identifying individual occurrences of theft or misconduct rather than understanding the underlying user behaviors that lead to these events, making it difficult for companies to effectively address inventory shrinkage.
Innovation Solution
A system that utilizes video cameras, video analytic modules, and computers to generate real-time investigations of user behavior by capturing video, processing it to generate non-video data, and analyzing this data to identify specific user behaviors, which can then be used to generate investigations containing video sequences of these behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If companies use passive electronic devices or video monitoring to identify individual theft occurrences, then they can detect specific theft events, but they cannot understand the underlying user behaviors that lead to these events
Solution Approach 1:
The system segments user behavior into discrete, analyzable components by defining specific behaviors (e.g., loitering, hiding items) as separate detectable events. This allows the system to break down complex theft patterns into individual behavioral segments that can be monitored and analyzed independently, enabling both precise theft detection and comprehensive behavioral understanding
Solution Approach 2:
The system transitions from two-dimensional video imagery to multi-dimensional behavioral data by extracting structured information about user actions, locations, times, and patterns. This dimensional transformation converts visual data into quantifiable behavioral metrics that reveal underlying patterns and relationships not visible in raw video alone
2Loss of information
If companies implement comprehensive video monitoring to understand user behaviors, then they can identify behavioral patterns, but the system complexity and processing requirements increase significantly
Solution Approach 1:
The system extracts only the essential behavioral features and patterns needed for loss prevention from the full video stream, rather than analyzing all video data in detail. By isolating and focusing on specific behavior types (loitering, hiding, swapping), the system achieves comprehensive behavioral understanding while minimizing processing complexity
Solution Approach 2:
The system applies partial analysis by monitoring only specific behaviors of interest rather than attempting to analyze all user actions comprehensively. This selective approach to behavioral analysis reduces processing requirements while still providing sufficient insight into theft-related patterns
3Measurement precision
If companies focus on identifying individual theft occurrences, then they can address specific incidents, but they are unable to address the underlying condition that allows individuals to commit theft
Solution Approach 1:
The system performs preliminary detection and analysis of suspicious behaviors before actual theft occurs by identifying patterns such as loitering, hiding items, or unusual shopping patterns. This early intervention capability allows companies to address underlying behavioral conditions proactively, preventing theft before it happens rather than merely reacting to completed incidents
Solution Approach 2:
The system establishes feedback loops that continuously monitor behavioral patterns and adjust monitoring focus based on identified risks. By feeding behavioral data back into the analysis system, the company can dynamically adapt to emerging theft patterns and address underlying conditions as they develop, improving overall shrinkage reduction efficiency
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. In some embodiments, the video camera is at least one of a traffic camera or an aerial drone 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.