Deep Learning ToF Behavior Detection with Anonymized Data Before Installation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies fail to proactively detect abnormal behaviors, such as the installation of hidden cameras, in areas requiring privacy, leading to reactive detection after crimes have occurred, compromising personal privacy and security.
Innovation Solution
A deep learning-based abnormal behavior detection system using a ToF sensor or thermal imaging sensor to generate anonymized image data, processed by a CNN and LSTM to analyze temporal changes, and a classification layer to determine normal or abnormal behavior, with warning signals transmitted to management or user terminals.
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 detection timing becomes reactive (after crime occurs) rather than proactive
Solution Approach 1:
The system performs preliminary action by detecting abnormal behavior patterns (such as prolonged staying, repeated entering/exiting, or unusual movement) before actual hidden camera installation occurs. The deep learning model is pre-trained with normal behavior patterns, enabling it to proactively identify deviations that may indicate preparation for criminal acts, thus preventing crimes before they happen rather than detecting them after occurrence.
2Measurement precision
If video recording is used to detect behavior, then detection accuracy is improved, but privacy protection is compromised
Solution Approach 1:
The system extracts only the essential behavioral features needed for detection while discarding all personally identifiable information. The deep learning model processes video data to extract motion patterns, posture, and behavior sequences, then discards the actual video content. Only anonymized behavior descriptors are retained and analyzed, separating detection functionality from privacy-sensitive data.
Solution Approach 2:
The system introduces an intermediary processing layer (the deep learning model) that acts as a mediator between video input and detection output. This intermediary transforms raw video data into abstract behavior representations, enabling accurate detection while preventing direct access to personal video content. The model serves as a buffer that protects privacy while maintaining detection capability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Proactively prevents crimes by detecting abnormal behaviors like hidden camera installations, ensuring personal privacy through anonymized data, and providing immediate warnings or emergency alerts.
Implementation Method 1
The detection device includes a time of flight (ToF) sensor
Data Source
AI summary
The present invention relates to a deep learning-based abnormal behavior detection system using anonymized data to sense abnormal behavior such as installing hidden cameras in a set area such as a bathroom. The present invention comprises: an anonymized image data generation unit for sensing the behavior of a subject in a set area, and generating image data in which personal information of the subject has been anonymized; and an abnormal behavior identification unit for categorizing the anonymized image data as normal or abnormal behavior data using a model trained with abnormal behaviors.