Video Surveillance Persistent Change Detection
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Solution Overview
Problem
Current video surveillance systems lack the ability to detect persistent changes within user-defined regions of interest and do not generate alerts or alarms in response to such changes, nor do they consider the transitory nature of detected objects.
Innovation Solution
A system and method that utilize a processor to identify a background model for a video stream, generate binary masks to detect persistent changes exceeding a predefined threshold, and produce change notification messages including location and temporal data, with the option to display or transmit these notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection is used in video surveillance, then general object detection capability is provided, but the system cannot detect persistent changes within specific regions of interest and generates excessive false alarms from transient events
Solution Approach 1:
The video stream is segmented into multiple frames, and each frame is compared against a background model to generate binary masks. The system then iteratively analyzes subsets of these masks over time to identify persistent changes, separating true threats from transient events through temporal segmentation.
Solution Approach 2:
A background model is pre-established for the video stream based on a predefined representation of regions of interest. This preliminary model serves as a reference for detecting changes, allowing the system to distinguish between normal variations and significant persistent changes before they become critical events.
2Productivity
If the system monitors all changes in the video stream, then comprehensive surveillance is achieved, but the system cannot distinguish between transient events and persistent changes
Solution Approach 1:
The system dynamically evaluates the persistence of detected changes by iteratively analyzing subsets of binary masks across multiple frames. Changes are classified as persistent or transient based on their duration and consistency over time, with the analysis adapting to the specific characteristics of each detected event.
Solution Approach 2:
The background model is continuously updated and refined as the system processes video frames. The iterative analysis of binary masks maintains continuous monitoring of regions of interest, ensuring that persistent changes are detected while transient events are filtered out through ongoing temporal analysis.
3Reliability
If the system generates alerts for all detected changes, then comprehensive monitoring is provided, but the alert system becomes overwhelmed with false positives from transient events
Solution Approach 1:
The system uses feedback from the iterative analysis of binary masks to determine whether detected changes warrant alert generation. By evaluating the persistence of changes across multiple frames and comparing against the background model, the system provides feedback-based filtering that prevents false alarms while maintaining reliable detection of genuine threats.
Data Source
AI summary
A method for monitoring protected regions of a camera view includes receiving, at a processor of a video camera system, a video stream including a series of video frames depicting at least one scene. A background model is identified based on a predefined representation of at least one region of interest associated with the scene. Each of the video frames is compared to the background model, to define an associated binary mask. A persistent change in the at least one region of interest is detected, based on the binary masks, the persistent change having an associated duration that exceeds a predefined threshold. In response to detecting the persistent change, a change notification message including a video file is generated. The method also includes causing display of the change notification message via a user interface and/or causing transmission of the change notification message to a remote compute device.