Abstract
In this paper, we propose a cascade classifier for high-performance on-road vehicle detection. The proposed system deliberately selects constituent weak classifiers that are expected to show good performance in real detection environments. The weak classifiers selected at a cascade stage using AdaBoost are assessed for their effectiveness in vehicle detection. By applying the selected weak classifiers with their own confidence levels to another set of image samples, the system observes the resultant weights of those samples to assess the biasing of the selected weak classifiers. Once they are estimated as biased toward either positive or negative samples, the weak classifiers are discarded, and the selection process is restarted after adjusting the weights of the training samples. Experimental results show that a cascade classifier using weak classifiers selected by the proposed method has a higher detection performance.
Highlights
Vehicle detection is a binary classification problem that distinguishes vehicles of different colors and shapes from cluttered backgrounds
Weak classifiers not biased toward either positive or negative samples are desired. This is due to the fact that false negatives are much more critical than false positives while objects are less frequent than non-objects in detection environments
The selected weak classifiers in conventional cascade classifiers are biased, and they lead to a gap between performance goal and observed performance at each stage
Summary
Vehicle detection is a binary classification problem that distinguishes vehicles of different colors and shapes from cluttered backgrounds. Vehicle detection is employed in driver assistance systems and in autonomous vehicles [1, 2]. In on-road detection environments, the background of images captured in a moving vehicle is not fixed and changes continuously. The ROI cannot be identified and the whole region of an image should be searched to detect vehicles. This means that a much larger number of operations is required. In applications related to on-road vehicle and transportation, driver safety is as important as convenience; a robust and real-time detection system should be capable of providing an instant alarm to the driver or system [1]. In. AdaBoost, a strong classifier is constructed by iterating rounds of weak classifier selection and sample weight updating. The weights are decreased for correctly classified samples and increased for incorrectly classified ones so that AdaBoost can focus on difficult samples [14, 15].
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