High-reliability Vehicle Detection and Lane Collision Warning System

Abstract

In the last two decades, developing Driving Assistance Systems for security has been one of the most active research fields in order to minimize traffic accidents. Vehicle detection is a vital operation in most of these applications. In this paper, we present a high reliable and real-time lighting-invariant lane collision warning system. We implement a novel real-time vehicles detection using Histogram of Oriented Gradient and Support Vector Machine which could be used for collision prediction. Thus, in order to meet the conditions of real-time systems and to reduce the searching region, Otsu's threshold method play a critical role to extract the Region of Interest using the gradient information firstly. Secondly, we use Histogram of Oriented Gradient (HOG) descriptor to get the features vector, and these features are classified using a Support Vector Machine (SVM) classifier to get training base. Finally, we use this base to detect the vehicles in the road. Two sets generated the training data of our system a set of negative images (non-vehicles) a set of positive images (vehicles), and the test is performed on video sequences on the road. The proposed methodology is tested in different conditions. Our experimental results and accuracy evaluation indicates the efficiency of your system proposed for vehicles detection.

Macroscopic and Microscopic Analysis of Chinese Typical Driving Behavior from UAV View

“…The drone is able to automatically decrease the vibration, although inevitable rotation still exists, due to wind in the sky. Deep learning methods were often used when performing vehicle detection and classification [28,29]. In this study, the trajectory generation workflow was shown in Figure 1, combing several deep learning methods.…”