Soccer Player Detection with only Color Features Selected Using Informed Haar-like Features

摘要

Player detection is important for tactical analysis, sports science, and video broadcasting, which is one of the practical applications of human detection. For human detection, filtered channel features shows better accuracy than methods based on deep learning. Considering the results on human detection, we constructed a detector having good balance between accuracy and computational speed for soccer players and using only color features to train a strong classifier. Experimental results using the PETS2003 dataset show that the proposed method can achieve about a 1.28 % miss rate at 0.1 FPPI, which is extremely good accuracy.