Video Patch-Wise VQ-Autoencoder for Low-Resolution Anomaly Detection
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Solution Overview
Problem
Intelligent CCTV systems face challenges in accurately detecting anomalies due to imbalanced data samples and low-resolution anomalies, leading to degraded performance and biased results.
Innovation Solution
A Video-based Patch-Wise Vector-Quantized Auto-Encoder (VBPVQAE) system that divides video frames into patches, learns a codebook through vector quantization, and uses a patch-wise discriminator network for anomaly detection, enabling accurate detection even with normal data and varying resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If deep learning is trained on imbalanced data with few anomaly samples, then the system can operate with limited anomaly data, but the detection performance degrades and results become biased toward normal samples
Solution Approach 1:
The video data is segmented into multiple patches, and the system learns normal representations at the patch level. This segmentation allows the model to focus on learning normal patterns without requiring anomaly samples, thereby resolving the contradiction between limited anomaly data and detection performance.
Solution Approach 2:
Instead of directly learning to detect anomalies, the system inverts the approach by learning to reconstruct normal video patches. Anomalies are detected by measuring the reconstruction error, which is high for anomalous patches. This inversion allows training with only normal data while maintaining high detection performance.
2Area of stationary object
If the system processes entire CCTV frames, then it captures complete scene information, but tiny anomalies remain undetected due to low resolution
Solution Approach 1:
The system segments the video into multiple patches and processes each patch independently through the autoencoder. This segmentation increases the effective resolution for anomaly detection while maintaining coverage of the entire scene, as each patch is processed with sufficient detail to detect tiny anomalies.
Solution Approach 2:
The system transitions from processing entire frames to processing patches, effectively adding a spatial dimension to the analysis. By examining multiple patches at patch-level resolution, the system achieves both comprehensive coverage and high detection precision for tiny anomalies.
3Reliability
If human-based CCTV monitoring is used, then anomaly detection can be performed, but the process becomes extremely tedious and time-consuming
Solution Approach 1:
The system implements self-service anomaly detection by automatically learning normal representations and detecting anomalies through reconstruction error measurement. This eliminates the need for human reviewers to manually examine video footage, maintaining high detection reliability while reducing monitoring time to near-zero for automated analysis.
Solution Approach 2:
The system replaces the mechanical process of human visual inspection with an automated deep learning-based autoencoder system. The mechanical action of human eyes and brain processing video is substituted with electronic processing through the neural network, dramatically reducing time loss while maintaining or improving detection reliability.
Data Source
AI summary
According to some embodiments, a system includes: a memory, an encoder; a decoder, wherein the system is operable to: receive, at the encoder, an input video; divide, by the encoder, the input video into a plurality of video patches; select, by the encoder, codes corresponding to the plurality of video patches of the input video, from a codebook comprising the codes; determine, by the encoder, an assigned code matrix comprising the codes corresponding to the plurality of video patches of the input video; receive, by the decoder, the assigned code matrix from the encoder; and generate, by the decoder, a reconstructed video based on the assigned code matrix.