Deep Learning Abnormal Behavior Detection via De-identified Image Analysis
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Solution Overview
Problem
There is a growing concern about hidden cameras being installed in private areas, such as restrooms and changing rooms, which infringe on individuals' privacy and can lead to illegal activities, as existing detection methods are often post-exposure and ineffective in preventing such crimes.
Innovation Solution
A deep learning-based system that includes a detection device generating de-identified image information, a deep learning server analyzing this data to predict behavior patterns, and a web server transmitting warning signals for abnormal behavior, utilizing CNN and LSTM for feature extraction and temporal analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a separate detector is used to detect hidden cameras, then detection capability is improved, but the detection occurs after the crime has already taken place (post-exposure)
Solution Approach 1:
The system performs preliminary action by detecting abnormal behaviors (such as unusual movements, objects left behind, or suspicious patterns) before hidden camera installation can occur or before criminal activity begins. The deep learning model analyzes behavior patterns in real-time and issues warnings before the actual crime takes place, transforming post-exposure detection into pre-crime prevention.
Solution Approach 2:
The system applies preliminary anti-action by proactively identifying and warning about suspicious behaviors that may precede hidden camera installation or criminal activity. The abnormal behavior detection creates a counter-effect that prevents the crime before it occurs, rather than merely detecting it afterward.
2Object-affected harmful factors
If de-identified image information is used for analysis, then privacy protection is improved, but the ability to detect abnormal behavior may be reduced
Solution Approach 1:
The system extracts only the necessary behavioral features (movement patterns, posture, sequence of actions) from the de-identified images while deliberately excluding personal identification information. The deep learning model is trained to recognize abnormal behaviors based on motion dynamics and spatial-temporal patterns rather than facial recognition or identity-specific features, maintaining detection accuracy while protecting privacy.
Solution Approach 2:
The system applies local quality by processing different aspects of the image data with different levels of detail. Personal identifying features are completely removed (de-identified), while behavioral characteristics (movement trajectories, body posture changes, interaction patterns with objects) are preserved and analyzed in detail. This selective processing maintains detection capability while ensuring privacy protection.
Data Source
AI summary
The present invention relates to a deep learning-based abnormal behavior detection system which detects abnormal behavior such as installation of a hidden camera in a predetermined area such as a restroom by using de-identification data. The present invention comprises: a de-identification image information generation unit which detects a subject's behavior in a predetermined area and generates image information data in which the subject's personal information is de-identified; and an abnormal behavior discrimination unit which classifies the de-identified image information data into normal behavior data or abnormal behavior data using a learning model for abnormal behavior.