Hand Detection

Hand tracking in this algorithm is implemented using OpenCV’s Haar cascade-based detection, where a grayscale image is processed to identify and track hand positions in real-time. The function first converts a 16-bit grayscale image into an 8-bit format, followed by a color conversion to RGB to enhance detection accuracy. A Haar cascade classifier (aGest.xml) is used to detect hand regions by scanning the grayscale version of the image. The detected bounding boxes are then refined by scaling and padding, ensuring that the hand’s position is accurately captured. The largest detected hand is selected, and its bounding box and center position are recorded.

Once a hand is detected, it is tracked across frames by updating the _trackedHands list, ensuring smooth tracking. The algorithm also estimates the distance of the hand from the camera by correlating depth values from the grayscale image, providing real-world depth estimation for hand positions. For visualization, a bounding box is drawn around the detected hand, and the estimated distance is overlaid as text. The processed image is then converted into a WriteableBitmap and displayed in the UI using Dispatcher, ensuring real-time updates. This efficient and scalable approach makes it ideal for gesture recognition, augmented reality (AR), virtual reality (VR), and human-computer interaction applications, where precise hand movement tracking is essential.