Robust Vehicle Detection by Using Deep Learning Feature and Support Vector Machine

Abstract

Vehicle detection and classification is an essential task in building an autonomous driving car. However, the accuracy of traditional vehicle detection systems is still not satisfied in real world due to unstable features from the original input images. This study introduces an efficient method to enhance the input feature more stable by considering the benefit of the machine learning approach to discover and extract robust features. The feature generated by the proposed deep learning model is then input to SVM to accurately identify objects. To evaluate the performance of the compared methods, we conducted comprehensive experiments on various datasets, such as KITTI, CCD, and HCI. For the KITTI dataset, the recall of our system, the LS-Support vector machine, the Linear support vector machine, the SVM-HOG, and the support vector machine are 71.4%, 64.3%, 53.8%, 67.8.0%, and 57.1%, respectively.