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Abstract
ABSTRACT This study examined the critical role of image color accuracy in surveillance systems, particularly in poultry houses, using affordable video cameras. Variations in commercial camera color processing can lead to inconsistent metrics. Therefore, common cameras were evaluated on the Raspberry Pi platform, aiming to minimize color differences through a three-stage correction process. The analysis without correction revealed significant color discrepancies, particularly in the PI cameras using an automatic white balance, with differences of approximately 50. Gamma correction was applied to improve accuracy, thereby reducing the color differences to within 20 for most cameras. Polynomial regression further decreased the differences to less than 10 across various temperatures, demonstrating superior performance, especially for large initial discrepancies. Field experiments with and without color charts confirmed the effectiveness of color restoration using correction matrices. The study concluded that polynomial regression significantly enhances color accuracy on the Raspberry Pi platform, offering valuable applications across different temperatures and scenarios, thereby contributing to advancements in related fields.
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