Thermal Image Anomaly Detection via Autoencoder Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing monitoring technologies for vulnerable individuals, such as the elderly, often infringe on privacy and require cumbersome wearable devices or high-resolution video cameras, which are not energy-efficient and require extensive data labeling for effective behavior recognition.
Innovation Solution
The use of thermal imaging cameras with autoencoders and unsupervised machine-learning algorithms to compress and cluster thermal image data, allowing for the detection of abnormal behavior without the need for video cameras or wearable devices, thus maintaining privacy and reducing computational power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If video cameras or wearable devices are used for monitoring, then behavior detection capability is improved, but privacy is compromised and device complexity increases
Solution Approach 1:
The patent extracts only the essential thermal information needed for behavior detection while discarding all other visual data. Thermal imaging cameras capture heat patterns and movement without recording visual appearance, thereby extracting the minimum necessary information for monitoring while preserving privacy.
Solution Approach 2:
The patent replaces optical/mechanical video camera systems with thermal imaging technology. This substitution fundamentally changes the detection mechanism from capturing visible light and images to detecting infrared radiation and heat patterns, enabling monitoring without visual privacy intrusion.
2Measurement precision
If high-resolution video cameras are used, then behavior recognition accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent employs low-resolution thermal imaging that captures sufficient behavioral information without the high energy costs of high-resolution video. The thermal data, while less detailed visually, provides adequate precision for detecting behavior patterns and anomalies with minimal computational and energy resources.
Solution Approach 2:
The patent changes the detection parameter from visual resolution to thermal contrast. By measuring temperature differences and heat patterns rather than visual details, the system achieves effective behavior recognition with significantly reduced energy requirements for both data capture and processing.
3Measurement precision
If traditional supervised learning is used for training, then behavior classification accuracy is improved, but data labeling effort increases
Solution Approach 1:
The patent implements self-service through unsupervised learning algorithms that automatically cluster thermal data without requiring manual labeling. The system autonomously identifies behavior patterns and categories by analyzing thermal signatures, eliminating the time-consuming data annotation process while maintaining classification accuracy.
Solution Approach 2:
The patent inverts the traditional supervised learning approach by using unsupervised learning. Instead of having algorithms learn from pre-labeled data, the system allows algorithms to automatically discover and create labels from raw thermal data, reversing the workflow to eliminate manual labeling requirements.
Data Source
AI summary
A computing system may train an autoencoder to generate a first set of codes from a first set of thermal video images of activities of a user in an environment. The activities may represent routine behaviors of the user in the environment. The computing system may use an unsupervised machine-learning algorithm to categorize the first set of codes into a set of clusters. The computing system may use the autoencoder to determine a code representative of a second set of thermal video images of an activity in the environment. Based on the code not being associated with any cluster in the set of clusters, the computing system may determine that the code is an anomalous code. The computing system may perform an alert action based on the anomalous code.