Water Region Detection in Video Surveillance
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Solution Overview
Problem
Current video surveillance systems are unable to automatically identify and adapt to water regions in video images, leading to inefficiencies in detection, tracking, and classification, and increased false alarm rates.
Innovation Solution
A computer-based method and system for automatically detecting water regions in video images, which involves generating a water map, estimating a statistical water model, re-classifying the water map, and refining it, to provide contextual information for improved event detection and target tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated video surveillance systems are used to monitor scenes, then detection rates are improved and human labor is saved, but the systems are unable to automatically identify water regions leading to increased false alarm rates
Solution Approach 1:
The system performs preliminary action by creating a water map that identifies water regions in advance before target detection occurs. This pre-processing step classifies pixels as water or non-water based on color, texture, and temporal characteristics, so that when targets are detected, the system already knows which regions are water, allowing it to filter out false alarms from water-based false detections.
Solution Approach 2:
The water map serves as an intermediary between the video input and the target detection processes. It acts as a mediator that provides contextual information about water regions, allowing the detection system to distinguish between actual targets and false alarms by referencing the pre-computed water region map.
2Duration of action of stationary object
If current video surveillance systems monitor the same scene for many years, then they accumulate data, but they are unable to adapt to variations in the scene caused by water areas
Solution Approach 1:
The system implements dynamics by making the water map adaptive and updateable. The water detection algorithm continuously refines its understanding of water regions by analyzing temporal variations and updating the water map accordingly. This allows the system to adapt to changing water conditions, such as varying water levels, wave patterns, and reflections, while maintaining long-term monitoring capability.
Solution Approach 2:
The system uses feedback mechanisms where the detected water regions are fed back into the monitoring process to continuously refine the water map. This feedback loop allows the system to learn from its own detections and improve its adaptability to water variations over time, enabling it to handle long-term monitoring scenarios with changing conditions.
3Device complexity
If water regions are not automatically identified, then the system remains simple, but contextual information about water and moving targets cannot be provided for improved event detection
Solution Approach 1:
The system applies segmentation by dividing the video image into water regions and non-water regions through the creation of a water map. This segmentation allows the system to process and analyze different regions of the image separately, providing contextual information about water areas while maintaining the ability to detect moving targets. The segmented approach enables sophisticated analysis without requiring complete redesign of the entire system.
Data Source
AI summary
A computer-based method for automatic detection of water regions in a video include the steps of estimating a water map of the video and outputting the water map to an output medium, such as a video analysis system. The method may further include the steps of training a water model from the water map; re-classifying the water map using the water model by detecting water pixels in the video; and refining the water map.