Machine Learning Package Theft Detection and Deterrence
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Solution Overview
Problem
Existing security systems that use cameras to monitor premises for security reasons are ineffective in preventing package theft since continuous monitoring by users is impractical.
Innovation Solution
Implementing a system that uses machine-learning models to identify packages and detect movement by individuals, triggering deterrence actions such as emitting light and sound to discourage theft.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cameras are used to monitor premises for security reasons, then security monitoring capability is improved, but the ability to prevent package theft deteriorates because users cannot continuously monitor the footage
Solution Approach 1:
The system enables the security camera to autonomously detect, analyze, and respond to package theft attempts without requiring continuous user monitoring. The camera system serves itself by incorporating machine learning models that automatically identify suspicious activities and trigger deterrence actions, transforming a passive monitoring tool into an active prevention system.
Solution Approach 2:
The patent replaces the mechanical system of continuous human monitoring with an automated digital system comprising machine learning models and algorithmic analysis. The machine learning models process video footage and sensor data to detect package theft attempts, substituting human cognitive effort with computational analysis that operates continuously without fatigue or distraction.
2Productivity
If users continuously monitor security camera feeds to identify and prevent package theft, then package theft prevention is improved, but user time and attention requirements worsen
Solution Approach 1:
The system autonomously performs the entire monitoring and response process without requiring user time investment. The machine learning models continuously analyze footage, detect suspicious activities, and execute deterrence actions automatically, allowing the system to serve itself in preventing package theft while freeing users from time-consuming monitoring tasks.
Solution Approach 2:
The automated system provides continuous monitoring and response capabilities without interruption, unlike human users who cannot maintain constant attention. The machine learning models process video feeds continuously, ensuring that package theft attempts are detected and deterred at any time, providing uninterrupted security coverage that eliminates gaps in protection.
3Productivity
If a machine-learning model automates the monitoring process to identify and deter package theft, then the ability to prevent theft is improved, but system complexity worsens
Solution Approach 1:
The system integrates multiple functions into a single unified platform: video capture, machine learning-based object detection, movement analysis, and deterrence action execution. By combining these previously separate functions into one multi-functional system, the patent reduces overall complexity while maintaining comprehensive package theft prevention capabilities.
Solution Approach 2:
The patent merges the machine learning model with the existing security camera infrastructure, combining detection algorithms with video capture and deterrence mechanisms into an integrated system. This consolidation eliminates the need for separate monitoring equipment and manual intervention processes, simplifying the overall system architecture while enhancing theft prevention effectiveness.
Data Source
AI summary
A method may include identifying, by a computer executing a machine-learning model, a moveable object located within an area, identifying, by the computer executing the machine-learning model, movement of the object by a person, and in response to the movement of the object by a person, executing, by the computer executing the machine-learning model, a deterrence action.