A Rapid Method Based on Vehicle Video for Multiobjects Detection

Abstract

An efficient and rapid method for car detection in video is presented in this paper. In this method, rear side view of cars is used in the detection phase. And in combination with histograms of oriented gradients (HOG) which is one of the most discriminative features, a linear support vector machine (SVM) is used for object classification. Besides, in order to avoid car missing, Kalman filter is used to track the objects. It is known that the calculation of HOG is complex and costs the most run time. So the processing time in this method is decreased by using information of objects' areas from the previous frames. It is shown by the experimental results that the detection rate can reach 96.20% and is more accurate when choosing the fit interval number such as 5. It is also illustrated that this method can decrease the calculating time on a large degree when the accuracy is about 94.90% by comparing with traditional method of HOG combining with SVM.