Rainy Video Object Segmentation via Edge-Change Detection

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Solution Overview

Problem

Existing video object segmentation methods face challenges in accurately segmenting moving objects in rainy environments due to noise and interference from raindrops, leading to errors in edge detection and object classification, particularly in real-time systems.

Innovation Solution

A method that combines edge detection and change detection using color spatial transformation and Gaussian smoothing filters to enhance the accuracy of video object segmentation, replacing motion estimation with change detection and utilizing a background model to differentiate between foreground and background, thereby reducing the impact of rain-related noise.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional video object segmentation methods are used in rainy environments, then processing speed can be maintained, but segmentation accuracy deteriorates due to raindrop noise and interference

Engineering Contradiction:

Improvesegmentation accuracyVSAvoidraindrop noise interference

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent segments the video processing into distinct stages: edge detection, change detection, and classification. By separating edge detection from change detection and processing them independently before combining results, the system can apply specialized filtering to each stage, improving overall segmentation accuracy in rainy conditions

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary classification stage that acts as a mediator between edge detection and change detection results. This classification step identifies and filters out raindrop-related edges from genuine object edges, eliminating the harmful effect of raindrop noise before final segmentation

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If motion estimation is used for video object segmentation, then temporal information can be utilized, but computational complexity increases

Engineering Contradiction:

Improveobject segmentation reliabilityVSAvoidcomputational complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent extracts only the necessary temporal information through change detection rather than performing full motion estimation. By taking out only the essential temporal changes needed for segmentation and discarding redundant computational steps, the system maintains reliability while reducing complexity

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent replaces the complex mechanical motion estimation system with a simpler change detection mechanism. Instead of calculating motion vectors and performing optical flow analysis, the system uses frame differencing and edge comparison, achieving similar segmentation reliability with significantly reduced computational complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If edge detection is performed in rainy conditions, then object boundaries can be identified, but false edges from raindrops increase detection errors

Engineering Contradiction:

Improveedge detection accuracyVSAvoidfalse edges from raindrops

Core Design Contradiction:

Measurement precisionVSObject-generated harmful factors

Solution Approach 1:

The patent applies dynamic filtering to edge detection by comparing edges across multiple frames and temporal positions. Genuine object edges maintain consistency over time while raindrop edges appear transiently, allowing the system to dynamically distinguish between true and false edges based on temporal behavior patterns

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent implements feedback mechanisms where classification results from one frame inform the processing of subsequent frames. By feeding back the identified raindrop patterns and false edge characteristics into the edge detection process, the system continuously refines its ability to distinguish genuine edges from raindrop-induced false edges

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS7860311B2Video object segmentation method applied for rainy situations

Publication Date: 2010.12.28 HUPER LAB

AI summary

A video object segmentation method takes advantage of edge and color features in conjunction with edge detection and change detection to improve the accuracy of video object segmentation for rainy situations. The video object segmentation method of the present invention includes analyzing HSI-color information of the initially extracted objects to obtain features of the moving object; performing edge detection to obtain edges of the moving object for reducing the effect of confusing raindrops with moving objects in rainy dynamic background; performing object region detection to generate an accurate object mask for solving the uncovered-background problem and the still-object problem; and employing a bounding-box based matching method for solving the reflection problem of the moving object in the rained ground.