Video Abnormal Segment Detection via Segmentation and PCA
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Solution Overview
Problem
Current video surveillance systems face inefficiencies in detecting abnormal events due to the reliance on model analysis, which struggles with dynamic backgrounds, leading to reduced accuracy.
Innovation Solution
A computerized system that divides video into segments, extracts features using Principal Component Analysis, differential operations, and Gaussian mixture models, and calculates abnormality scores to identify significant events regardless of background motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cluster analysis is used to detect abnormal parts of video, then reliability is improved, but measurement precision deteriorates when dynamic background is present
Solution Approach 1:
The video is divided into multiple segments based on background change detection. Each segment is then independently processed to extract features and calculate abnormality scores. This segmentation allows the system to handle dynamic backgrounds by treating each segment separately, preventing background changes from interfering with abnormal event detection in other segments.
Solution Approach 2:
The system changes the parameter of background motion status to create stationary background segments. By detecting background changes and creating segments based on these changes, the system transforms the dynamic background problem into a series of relatively stable segments, enabling accurate feature extraction and abnormality detection.
2Productivity
If model analysis is used to detect abnormal events, then productivity is improved, but measurement precision deteriorates due to inability to handle dynamic backgrounds
Solution Approach 1:
The video is divided into segments based on background stability. This segmentation enables the system to maintain high processing efficiency while improving detection accuracy, as each segment can be processed independently with appropriate methods suited to its characteristics.
Solution Approach 2:
The system transforms the background motion parameter to create stationary segments, allowing model analysis to function effectively. By changing the background from dynamic to stationary through segmentation, the system achieves both high productivity and accurate detection.
3Measurement precision
If all video data is reviewed manually to find unusual events, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system extracts and identifies abnormal segments automatically from the video data. By using feature extraction and abnormality score calculation, the system can quickly identify and separate useful abnormal events from normal video content, eliminating the need for manual review of entire video datasets.
Solution Approach 2:
The system calculates abnormality scores for each segment and uses this feedback to automatically identify and prioritize abnormal events. This feedback mechanism enables the system to efficiently filter and present only the most relevant abnormal segments, saving time while maintaining high detection accuracy.
Data Source
AI summary
A system and a method of detecting abnormal or security-significant segments of a video are provided. The video is divided into several segments. A set of features of each segment of the video is extracted in order to calculate a set of factors corresponding to each segment. A value deemed abnormal of each segment is calculated according to the set of factors corresponding to each segment. One or more abnormal segments are determined from the segments based on the abnormal values. The set of features includes a color variable feature, a movement variable feature, a movement variable ratio feature and a background variable feature.