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Nowadays, real-time vehicle detection is one of the biggest challenges in driver-assistance systems due to the complex environment and the diverse types of vehicles. Vehicle detection can be exploited to accomplish several tasks such as computing the distances to other vehicles, which can help the driver by warning to slow down the vehicle to avoid collisions. In this paper, we propose an efficient real-time vehicle detection method following two steps: hypothesis generation and hypothesis verification. In the first step, potential vehicles locations are detected based on template matching technique using cross-correlation which is one of the fast algorithms. In the second step, two-dimensional discrete wavelet transform (2D-DWT) is used to extract features from the hypotheses generated in the first step and then to classify them as vehicles and nonvehicles. The choice of the classifier is very important due to the pivotal role that plays in the quality of the final results. Therefore, SVMs and AdaBoost are two classifiers chosen to be used in this paper and their results are compared thereafter. The results of the experiments are compared with some existing system, and it showed that our proposed system has good performance in terms of robustness and accuracy and that our system can meet the requirements in real time.

Real-Time Vehicle Detection Using Cross-Correlation and 2D-DWT for Feature Extraction