Detecting pedestrians and vehicles in traffic scene based on boosted HOG features and SVM

This paper presents a popular method called boosted hog features to detect pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we selected boosted hog features to get an satisfying result. In the part of detecting pedestrians, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good performance in various kinds of background. On that basis, we create a new feature with boosting algorithm to obtain more accurate results. In the part of detecting vehicle, we select the shadow underneath vehicle as the feature, so we can utilize it to detect vehicles in daytime. The shadow is an important feature for vehicles in traffic scenes. The region under vehicle is usually darker than other objects or backgrounds and can be segmented by setting a threshold. Finally, experimental results show that the detection of pedestrians and vehicles using boosted hog feature and linear svm combines the advantages of hog feature and adaboost classifier, and can achieve better detection results than the detector using conditional HOG features. At the end, the paper shows its efficiency and effectiveness using to application in real situations.