Transformer Anomaly Detection Pyramid Encoder
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional deep learning technologies for anomaly detection in surveillance camera images fail to adequately reflect temporal characteristics, making it inefficient to monitor and analyze numerous images for unexpected events.
Innovation Solution
A transformer-based anomaly detection apparatus that utilizes a pyramid-structured encoder to extract multi-scale feature maps, incorporating spatio-temporal transformers to generate attention maps that emphasize temporal and spatial features, and a decoder with a reverse pyramid structure for frame prediction, enabling the detection of abnormal events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional deep learning technology is used for anomaly detection, then the system can process surveillance images, but it fails to reflect temporal characteristics and cannot properly detect anomalies over time
Solution Approach 1:
The patent introduces a temporal dimension to the anomaly detection system by processing video frames sequentially through the transformer encoder. The model captures temporal dependencies by analyzing the sequence of frames, transforming the detection from a static image analysis to a dynamic video analysis that understands how anomalies evolve over time.
Solution Approach 2:
The transformer encoder processes video frames continuously in temporal sequence, maintaining context information across multiple frames. The self-attention mechanism continuously references previous frames when analyzing current frames, ensuring that temporal characteristics and evolving patterns are preserved throughout the detection process.
2Reliability
If a transformer-based model with spatio-temporal attention is used, then long-range dependency and spatio-temporal features can be captured, but the model complexity increases
Solution Approach 1:
The patent divides the video processing into discrete frame segments that are processed independently through the transformer encoder. Each frame is processed as a separate input to the encoder, which then analyzes temporal relationships between these segmented frames. This segmentation allows the complex spatio-temporal attention to be applied systematically across manageable units.
Solution Approach 2:
The transformer encoder serves multiple functions simultaneously: it processes spatial information within each frame, captures temporal relationships across frames, and generates attention maps that highlight both spatial and temporal patterns. This multi-functionality reduces the need for separate specialized modules, managing overall system complexity.
Data Source
AI summary
A transformer-based anomaly detection apparatus includes: an encoder unit including n encoding blocks having a pyramid structure, and extracting respective multi-scale feature maps having the pyramid structure through respective encoding blocks; a spatio-temporal transformer unit having n spatio-temporal transformers, and generating each multi-scale spatio-temporal attention map in which a temporal feature and a spatial feature for the feature map extracted from each encoding block are emphasized through each spatio-temporal transformer; and a decoder unit including n decoders having a reverse pyramid structure, and generating a prediction frame by using outputs of the multi-scale spatio-temporal attention map and a previous layer decoder through n decoders, and an abnormal event is detected by using the prediction frame.