Motion Vehicle Detection and Tracking Using Threshold Segmentation
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This approach utilizes threshold segmentation for motion vehicle detection and tracking, performing image segmentation in videos by dynamically adjusting grayscale threshold values and subsequently labeling the segmented regions. The method involves converting video frames to grayscale, applying adaptive thresholding algorithms (such as Otsu's method) to separate vehicles from background, and using connected component analysis for object labeling. This technique effectively detects and tracks moving vehicles, holding significant importance for applications in traffic monitoring systems and autonomous driving technologies. Implementation typically involves OpenCV functions like cv2.threshold() for segmentation and cv2.connectedComponents() for object labeling and tracking.
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