研究目的
To develop an image processing based indoor localization system using color detection to assist visually impaired people in navigating indoor environments with accuracy and real-time operation.
研究成果
The system successfully provides accurate indoor localization with a maximum error of 1cm and average processing time of 0.08s, offering a reliable and cost-effective solution for assisting visually impaired people. Future work includes expanding to larger areas and optimizing processing speed.
研究不足
Line of sight can be a limitation; processing time may increase with server load; system performance depends on camera quality and lighting conditions.
1:Experimental Design and Method Selection:
The system uses image processing with OpenCV and Python for color detection and localization. A server handles heavy processing, and a smartphone app connects users. Color allocation ensures unique identification.
2:Sample Selection and Data Sources:
Experiments were conducted in an indoor room using a webcam to capture images. Users are assigned unique colors via the app.
3:List of Experimental Equipment and Materials:
A normal webcam (model not specified), server computer, smartphones with the app, and Firebase for real-time database operations.
4:Experimental Procedures and Operational Workflow:
Images are captured every 1 second, converted to HSV color space, processed for color masks, and analyzed for connected objects. Location is determined using a grid matrix, and distance is calculated based on pixel mapping.
5:Data Analysis Methods:
Processing time and distance accuracy are measured, with errors calculated manually and through the system.
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