Abnormal Motion Detection in Video Surveillance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Video surveillance systems face inefficiencies due to human operators' fatigue and difficulty in detecting abnormal motion in real-time, leading to delayed response times and inefficient investigation processes, especially in environments with numerous cameras.
Innovation Solution
An apparatus and method for detecting abnormal motion in video streams using motion vectors and statistical models, which extracts and compares motion features to identify deviations from normal patterns, generating alerts for significant anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators watch surveillance screens to detect security breaches, then immediate response to security incidents can be facilitated, but operators experience information overload and fatigue leading to missed detections
Solution Approach 1:
The patent replaces the mechanical human visual inspection system with an automated computer-based motion analysis system. The system extracts motion vectors from video frames, builds statistical models of normal motion patterns, and automatically detects anomalies through algorithmic comparison, eliminating operator fatigue while maintaining detection accuracy.
Solution Approach 2:
The system enables self-service by allowing the surveillance system to automatically monitor itself without human intervention. The automated motion analysis system continuously processes video streams, detects abnormal motion patterns, and generates alerts independently, making the system self-sufficient in detecting security breaches.
2Area of stationary object
If operators monitor multiple cameras on split screens, then comprehensive coverage is achieved, but the number of monitors is much smaller than cameras and operators miss up to 95% of scene activity after 22 minutes
Solution Approach 1:
The system extracts only the critical motion information from video streams using motion vector analysis. By focusing on motion patterns rather than displaying all video feeds, the system identifies and alerts operators to specific abnormal activities, eliminating the need to monitor all cameras continuously and reducing response time.
Solution Approach 2:
The system changes the monitoring parameter from visual inspection of video frames to analysis of motion vector statistics. By transforming the data representation from pixel-based video to motion-based statistical models, the system can process multiple camera streams efficiently and detect anomalies without operator fatigue.
3Loss of information
If recorded video data is stored for investigation, then event review is possible, but locating and reviewing recorded footage is hard and tedious
Solution Approach 1:
The system performs preliminary action by automatically analyzing and tagging video data during the recording phase. Motion patterns are extracted and stored as metadata alongside video footage, enabling rapid retrieval and investigation without manual review of entire video streams, thus preserving evidence while reducing investigation time.
Data Source
AI summary
An apparatus and method for detection of abnormal motion in video stream, having a training phase for defining normal motion and a detection phase for detecting abnormal motions in the video stream is provided. Motion is detected according to motion vectors and motion features extracted from video frames.