Real-Time Motion Tracking and Counting via USB Webcam Image Acquisition

This system captures images through a USB webcam connected to a computer and processes them in real-time to track moving objects and maintain count statistics. For background modeling, we implement a median filtering approach that calculates the statistical median of recent frames to generate a reference background image, which can be periodically updated to adapt to environmental changes. The video processing pipeline employs a hybrid method combining dynamic frame differencing (comparing consecutive frames) with foreground detection (comparing current frame against the background model). This dual approach effectively identifies moving objects while minimizing false positives from gradual illumination changes. However, the current implementation faces limitations in handling object occlusion scenarios where multiple persons overlap, as the algorithm cannot reliably distinguish merged foreground blobs into individual entities. The processing workflow typically involves OpenCV functions like cv2.VideoCapture() for frame acquisition, cv2.medianBlur() for noise reduction, and contour detection methods for object tracking and counting operations.