Monitoring Device Attribute Change Detection for Disguised Objects
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Solution Overview
Problem
Existing monitoring systems fail to detect suspicious objects when individuals disguise themselves or change their clothing or gait, as these changes are not inherently determined as suspicious behaviors.
Innovation Solution
A monitoring device that associates objects across time-series images, detects attribute changes in objects and accessories, and identifies suspicious objects based on these changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If behavior-based suspicious object detection is used, then detection of suspicious actions is improved, but detection of disguised objects is worsened
Solution Approach 1:
The patent segments the detection process into two independent modules: behavior analysis (detecting suspicious actions) and attribute change analysis (detecting disguises). The attribute change detection unit separately monitors changes in object characteristics such as clothing, appearance, and accessories, independent of behavior analysis. This segmentation allows the system to detect both suspicious behaviors and disguises without one method limiting the other.
Solution Approach 2:
The patent adds a new dimension to detection by introducing attribute change analysis as a separate detection axis alongside traditional behavior analysis. Instead of relying solely on temporal behavior patterns, the system now monitors dimensional changes in object attributes (clothing type, color, accessories) over time. This dimensional expansion enables detection of objects that change appearance to conceal their identity or intent.
2Reliability
If clothing change detection is added to tracking, then person re-identification is improved, but false alarms increase
Solution Approach 1:
The patent implements dynamic thresholding and context-aware detection for attribute changes. Rather than treating all attribute changes as suspicious, the system adapts its detection sensitivity based on contextual information and historical patterns. The suspicious object detection unit dynamically adjusts detection criteria to distinguish between normal attribute changes (such as taking off a coat in a warm environment) and suspicious changes (such as rapid clothing changes in secure areas).
Solution Approach 2:
The system incorporates feedback mechanisms where detection results from both behavior analysis and attribute change analysis are integrated and cross-validated. The suspicious object detection unit receives feedback from both detection modules and adjusts its determination based on the combined evidence. This feedback loop reduces false alarms by requiring corroboration between different detection approaches before generating an alert.
3Adaptability or versatility
If multiple detection criteria are combined, then detection comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex detection system into distinct functional modules: behavior analysis unit, attribute change detection unit, and suspicious object detection unit. Each module has a specific function and processes data independently before integration. This modular segmentation reduces system complexity by making each component manageable and independently developable, while the overall system achieves comprehensive detection capability through the combination of modules.
Solution Approach 2:
The suspicious object detection unit serves as a universal integration point that receives and processes outputs from both behavior analysis and attribute change detection modules. This multi-functional unit consolidates the detection logic and provides a unified output interface, reducing the need for separate complex integration systems and simplifying the overall architecture while maintaining comprehensive detection capabilities.
Data Source
AI summary
A monitoring device and the like are provided which are capable of detecting an attribute change in a suspicious object that cannot be determined from the behavior of the object. An associating unit associates, among a plurality of objects detected from time-series image data, identical objects with one another. An attribute change detecting unit detects from the time-series image data a change in an attribute of at least one of the identical objects and an attendant item. A suspicious object detecting unit detects a suspicious object on the basis of the change in attribute.