First of all, we studied three different types of feature operators in the application of image-based pedestrian detection. They are Histogram of Oriented Gradient(HOG), Local Ternary Patterns(LTP) and the Sqrt Local Ternary Patterns(S-LTP). Through conducting experiments to compare these three feature operators, we combined the algorithms of HOG and S-LTP and proposed a new human detection feature operator named HOG+S-LTP. Finally, by using a linear SVM as the classifier, we compared the new feature with the other single features in INRIA person dataset. Results of experiments showed that the new combined feature operator significantly improves the pedestrian detection rate, and it also meets real-time requirements.