Adaptive Object Segmentation in Video Surveillance
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Solution Overview
Problem
Current video surveillance systems face challenges in accurately segmenting objects in images, especially due to varying object distances, which affects the robustness and effectiveness of image processing algorithms used for object detection and tracking.
Innovation Solution
A device with an estimation module to calculate object pixel distances and a segmentation module that adapts segmentation strategies based on estimated distances, incorporating scene knowledge to enhance object segmentation, using methods such as pixel-oriented, edge-oriented, region-oriented, texture-based, and color-oriented approaches, and allowing for iterative refinement of segmentation based on updated distance estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed segmentation strategy is used for all objects regardless of distance, then the device complexity is reduced, but the segmentation precision deteriorates due to varying object distances affecting detection accuracy
Solution Approach 1:
The patent applies dynamics by making the segmentation strategy adaptive rather than fixed. The system dynamically adjusts segmentation parameters based on estimated object distance, allowing the segmentation approach to change according to the specific conditions of each object in the scene.
Solution Approach 2:
The patent changes segmentation parameters based on object distance. Different segmentation strategies are selected depending on whether objects are near or far, with parameters such as threshold values and processing methods being adjusted according to the distance estimates obtained through scene knowledge.
2Measurement precision
If distance estimation is performed for all pixels, then the segmentation accuracy is improved, but the processing time increases due to additional computational steps
Solution Approach 1:
The patent performs distance estimation as a preliminary step before segmentation, using scene knowledge to obtain distance information. This preliminary action allows the subsequent segmentation process to be optimized based on pre-acquired distance data, rather than performing all computations simultaneously.
Solution Approach 2:
The patent segments the processing based on distance zones. By dividing the scene into near and far object regions based on distance estimates, different segmentation approaches can be applied to different regions, reducing the overall computational burden compared to uniform processing of all pixels.
3Adaptability or versatility
If multiple segmentation strategies are maintained for different distances, then the adaptability is improved, but the device complexity increases due to multiple strategies needing management
Solution Approach 1:
The patent applies local quality by using different segmentation strategies for different spatial regions (near vs. far objects). Each region receives a segmentation approach optimized for its specific characteristics, with near objects using one strategy and distant objects using another, rather than applying a single uniform strategy throughout.
Solution Approach 2:
The system dynamically selects appropriate segmentation strategies based on distance estimates. Rather than maintaining all strategies simultaneously active, the system adapts by selecting and applying only the relevant strategy for each object based on its distance, reducing the operational complexity while maintaining adaptability.
Data Source
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AI summary
The invention relates to a device 2 for segmenting an object 10, 11 in an image 9, wherein an area under surveillance with one or more objects 10, 11 is shown and/or can be shown in the image 9, with an estimation module 5, which is configured to estimate a distance in the area under surveillance from a possible object pixel 12a,b of the object 10, 11 in the image 9, with a segmentation module 7, which is configured to segment the object 10, 11, which includes the object pixel 12a,b, according to a segmentation strategy, and with a strategy module 6, which is configured programmatically and/or circuit-wise, to adapt the segmentation strategy for the object 10, 11 depending on the estimated distance.