Video People Detection Using Clothing Color Segmentation
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Solution Overview
Problem
Current people detection systems using video cameras struggle to accurately detect individuals within facilities due to various human activities, cluttered backgrounds, camera characteristics, and illumination changes, and cannot distinguish between different types of people based on limited information.
Innovation Solution
A method and apparatus that utilize a video analytic device to detect people within video frames by identifying multiple colors in their clothing, applying color and geometric constraints to determine the count of individuals wearing specific uniforms, and smoothing counts to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional people detection systems use video cameras to monitor areas, then they can detect people at building entrances, but they cannot detect people inside facilities or distinguish between different types of people
Solution Approach 1:
The system segments the detection process into multiple stages: color detection, geometric constraint verification, and classification. By dividing the complex detection task into manageable segments, the system can accurately identify people inside facilities and distinguish between different types of people (e.g., employees vs. visitors) based on their clothing colors and patterns.
Solution Approach 2:
The invention utilizes color changes and color-based features as the core detection mechanism. By analyzing the colors of clothing worn by people in video frames, the system can identify and classify different types of people. The system detects specific color combinations and patterns that correspond to different uniform types, enabling accurate people classification without requiring additional sensors or infrastructure.
2Adaptability or versatility
If video cameras capture wide variety of human activities and movements, then they can monitor diverse scenarios, but detection accuracy decreases due to cluttered backgrounds and illumination changes
Solution Approach 1:
The system applies local quality by focusing detection on specific local features - the colors and geometric patterns of clothing. Instead of analyzing the entire scene or relying on global features that may be affected by background clutter and lighting changes, the system concentrates on the stable, distinctive color characteristics of uniforms. This local focus enables accurate detection across diverse scenarios while maintaining precision.
Solution Approach 2:
The invention changes the detection parameter from traditional methods (such as motion detection or template matching) to color-based detection. By shifting to color space analysis and detecting color combinations that are invariant to illumination changes, the system maintains high accuracy across varying lighting conditions and diverse human activities. The system transforms the detection problem into a color classification problem that is more robust to environmental variations.
3Device complexity
If existing detection systems rely on limited video camera information, then they are simple to implement, but they cannot distinguish between different types of people
Solution Approach 1:
The system achieves multi-functionality by using a single video camera to perform multiple detection tasks: detecting people's presence, determining their locations, and classifying their types based on clothing colors. The color-based detection framework is universally applicable to different types of people and can be configured to recognize various uniform combinations. This universal approach eliminates the need for multiple specialized sensors or complex hardware modifications while enabling comprehensive people classification.
Data Source
AI summary
A video analytic device performs a method for detecting people within frames of video based upon multiple colors within their clothing. The method includes: receiving a frame of video; and determining that a first color region within the frame matches a first color of interest for a clothing uniform, wherein the determining is based on a first set of color representation constraints. The method further includes determining that a second color region within the frame matches a second color of interest for the clothing uniform, wherein the determining is based on a second set of color representation constraints and the first and second colors of interest are different. In addition, the method includes applying a set of geometric constraints to the first and second color regions to determine a count of people within the frame wearing the clothing uniform.