Video Anomaly Detection via Normal Activity Exemplars
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Solution Overview
Problem
Existing video anomaly detection methods struggle to distinguish rare but normal activities from abnormal activities, often resulting in false positives and missed anomaly detections due to their reliance on reconstruction and frame-prediction approaches.
Innovation Solution
A system that forms a model of normal activity by storing a set of feature vectors (exemplars) representing normal activity in a scene, and then compares feature vectors from input video frames to these exemplars to detect anomalies based on distance thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reconstruction-based methods are used to detect anomalies, then the system can identify unusual patterns, but rare but normal activities are misclassified as anomalies resulting in false positives
Solution Approach 1:
Instead of trying to detect anomalies directly by learning what is unusual, the patent inverts the approach by learning to detect only normal activities. The system trains on normal video data to create a model of typical behavior, and anything that does not match this learned normal behavior is flagged as anomalous. This inversion resolves the contradiction by preventing false positives from rare normal activities, since the model explicitly learns what normal varies look like rather than trying to identify all possible anomalies.
2Measurement precision
If deep neural networks are trained for each new scene, then detection accuracy improves for that scene, but the system becomes difficult to use in practice due to retraining requirements
Solution Approach 1:
The patent creates a universal anomaly detection system that can be applied to multiple different scenes without retraining. The model learns scene-specific normal behaviors during a brief adaptation phase using only normal video from the target scene, rather than requiring extensive retraining. This universality allows the same core system to serve multiple functions across different surveillance locations while maintaining high detection accuracy for each specific scene.
3Measurement precision
If manual inspection of recorded video data is performed, then detailed analysis is possible, but the process is very laborious and events may go unnoticed
Solution Approach 1:
The patent introduces an automatic anomaly detection system as an intermediary between video recording and human inspection. The system automatically analyzes video data, detects anomalies, and flags only suspicious events for human review. This intermediary layer filters out normal activities, allowing operators to focus their time on actual anomalies while maintaining thorough detection capability.
Data Source
AI summary
A system for video anomaly detection is configured to extract, from a set of input frames of the input video, input appearance features indicative of the appearance of the object in a frame, input size features indicative of the size of the object in the scene, input location features indicative of the location of the object in the scene, and input trajectory features indicative of a trajectory of the object tracked in a set of frames of the input video. The system combines the input appearance features, the input size features, the input location features, and the input trajectory features to produce an input feature vector and compares the input feature vector with each of the exemplars extracted from the normal video to determine the smallest distance from the input feature vector to its closest exemplar. The system declares the anomaly when the smallest distance is greater than a threshold.