Unsupervised Video Anomaly Segmentation Using Uncertainty Estimation
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Solution Overview
Problem
Existing video anomaly detection (VAD) methods operate as black boxes, lack understanding of frame-level determinations, rely on subjective handcrafted features, and struggle with extrapolating to unknown anomaly types, while being computationally complex and memory-intensive.
Innovation Solution
The implementation of unsupervised video anomaly segmentation (UVAS) using uncertainty estimation techniques for temporal detection and pixel-level segmentation, compatible with various pretrained networks, including transformers, to provide both anomaly detection and segmentation at the video frame level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional VAD methods use handcrafted features and deep learning models, then anomaly detection capability is provided, but the system operates as a black box without understanding frame-level determinations and requires high computational complexity and memory
Solution Approach 1:
The patent segments the video data into individual frames and processes each frame independently through the neural network. This frame-level processing divides the complex video analysis task into smaller, manageable units, reducing the computational burden on the system while maintaining anomaly detection capability.
Solution Approach 2:
The patent replaces traditional handcrafted feature extraction mechanisms with a neural network-based automated feature learning system. This substitution eliminates the need for manual feature engineering and reduces the computational complexity associated with processing handcrafted features across multiple deep learning models.
2Reliability
If existing VAD methods process video data, then anomaly detection is performed, but they lack understanding of frame-level determinations and cannot extrapolate to unknown anomaly types
Solution Approach 1:
The patent performs preliminary feature extraction and anomaly scoring at the frame level before final anomaly determination. This preliminary processing at each frame enables the system to build up understanding incrementally and make informed decisions about anomalies while maintaining the ability to detect previously unseen anomaly types through learned patterns.
Solution Approach 2:
The patent transforms video data into a different parameter space through neural network processing, converting raw pixel data into meaningful feature representations. This parameter transformation enables the system to detect anomalies based on learned patterns rather than predefined rules, improving adaptability to unknown anomaly types.
3Productivity
If VAD systems analyze video streams in real-time, then monitoring capability is provided, but computational resources are heavily consumed
Solution Approach 1:
The patent segments video analysis into frame-level independent processing units, allowing for efficient parallel computation and optimized resource utilization. This segmentation enables real-time processing by breaking down the continuous video stream into discrete, manageable frames that can be processed sequentially or in parallel batches.
Solution Approach 2:
The patent extracts only the essential features needed for anomaly detection at each frame using the neural network, rather than processing all raw video data. This extraction of critical information reduces the computational load and energy consumption while maintaining the ability to detect anomalies in real-time.
Data Source
AI summary
Methods and apparatus to detect anomalies in video data are disclosed. An example apparatus disclosed herein generates a reconstructed feature vector corresponding to an input feature vector representative of a video segment, the reconstructed feature vector based on a transformation applied to the input feature vector and an inverse of the transformation applied to an output of the transformation, the input feature vector and the reconstructed feature vector including features associated with a plurality of dimensions including a time dimension. The disclosed example apparatus also generates an error vector based on a difference between the input feature vector and the reconstructed feature vector. The disclosed example apparatus further generates an anomaly map based on sums of elements of the error vector across at least the time dimension, the anomaly map corresponding to the video segment.