Motion Vehicle Detection Using Background Subtraction Method

In motion vehicle detection systems, the background subtraction method serves as a fundamental approach. This technique operates by comparing the current video frame with a static background model to identify moving objects, such as automobiles. The implementation typically involves creating a background model through methods like frame averaging or Gaussian mixture models (GMM), followed by thresholding the difference image to obtain foreground objects.

However, the vehicle images obtained through background subtraction often contain noise and extraneous information. To address this, image preprocessing operations are essential before vehicle detection. These may include morphological operations (opening/closing) to remove small artifacts, Gaussian blur for noise reduction, and contrast enhancement techniques. Additionally, edge extraction algorithms like Canny or Sobel operators are applied to highlight vehicle contours and structural features. These preprocessing steps significantly optimize detection results by improving the signal-to-noise ratio and enhancing relevant features, ultimately increasing both detection accuracy and system reliability.