Activity Summary Generation for Retail Theft Detection
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Solution Overview
Problem
Existing automatic tracking systems in commercial sites are prone to raising false alarms or missing theft activity due to their sensitivity settings, leading to inefficiencies in detecting suspicious actions.
Innovation Solution
A system and method that generates an activity summary by receiving videos from image capture devices, creating a video-loop of a person's trip, identifying suspicious actions through an analysis module, and producing an action clip, which is then integrated into a comprehensive summary to enhance theft detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the automatic tracking system is constructed to be very sensitive to events that raise alarms, then the detection of suspicious actions is improved, but false alarms increase causing inconveniences to customers and security personnel
Solution Approach 1:
The system segments the surveillance process into multiple analysis stages: initial event detection, video-loop generation, action recognition, and suspicious action classification. By dividing the detection process into discrete segments with specific functions, the system can apply different sensitivity thresholds at each stage, reducing false alarms while maintaining reliable suspicious action detection.
Solution Approach 2:
The system introduces an intermediary analysis layer between raw video input and alarm output. The video-loop generation and action recognition modules serve as intermediaries that filter and interpret events before triggering alarms. This intermediary processing reduces false alarms by requiring multiple conditions to be met before generating an alarm, while still maintaining high sensitivity for actual suspicious actions.
2Object-generated harmful factors
If the automated system lowers the sensitivity, then false alarms are reduced, but a substantial amount of theft activity is missed
Solution Approach 1:
The system dynamically adjusts sensitivity thresholds based on the analysis stage and context. Early detection uses higher sensitivity to capture all potential events, while subsequent action recognition and classification stages apply more specific, context-aware thresholds. This dynamic adjustment allows the system to maintain high detection reliability without generating false alarms at each stage.
Solution Approach 2:
The system implements feedback loops where detection results from one stage inform the sensitivity settings of subsequent stages. Action recognition feedback adjusts the thresholds for suspicious action classification, and alarm history feedback modifies detection parameters. This feedback mechanism ensures that lowering sensitivity in one stage does not compromise overall detection reliability, as subsequent stages compensate with enhanced analysis.
3Measurement precision
If the system processes all video data in detail, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments video processing into hierarchical levels: rapid event detection on key frames, selective video-loop generation for events of interest, and detailed action recognition only for segmented action clips. This segmentation allows detailed analysis only where necessary, maintaining high detection accuracy while minimizing overall processing time and computational resource consumption.
Solution Approach 2:
The system applies partial detailed analysis only to relevant portions of video data. Instead of processing all video data in full detail, it performs comprehensive action recognition only on video-loops containing detected events, and further detailed analysis only on segments containing potential suspicious actions. This partial action approach maintains detection accuracy for critical events while significantly reducing overall processing time.
Data Source
AI summary
In accordance with one aspect of the present technique, a method includes receiving one or more videos from one or more image capture devices. The method further includes generating a video-loop of the person from the one or more videos. The video-loop depicts the person in the commercial site. The method also includes generating an action clip from the video-loop. The action clip includes a suspicious action performed by the person in the commercial site. The method further includes generating an activity summary of the person including the video-loop and the action clip.