Target Detection in Video Images Using HOG-Based Cascade Classifier

Detecting small objects using computer vision is a challenging task due to their small size in the image and therefore the lack of features when describing them. In this paper, a computer was trained to detect three small balls using 20 levels of the AdaBoost cascade classifier. The features of the balls in each level are described using the HOG feature descriptor. Three balls were recorded in practice at various distances (d = 2, 3, 4, ..., 10 m) from the camera and the targets (balls). The frames are then taken from the videos and resized using five magnification factors (RS = 1, 3, 5, 7, and 9) to make the balls seem as they should. According to the results, the detection rate of balls at all distances was 80% when using the magnification factor RS = 1, 90% when using the magnification factor RS = 3, 5, and 7, and 100% when using the magnification factor RS = 9. The suggested approach was also used in calculating the height and width of the detected balls. The overall results indicated that the height and width of the balls dwindle as the distance between the camera and the targets increases.