The proposed classification system which includes AdaBoost vehicle detector and calibration method we proposed can do classification well. At AdaBoost vehicle detector stage, the prime priority is to detect and verify the foreground get from Gaussian Mixture Model whether is a vehicle. At the calibration stage, the top mission is to record the information of the extracted foreground, such as width and height in the world coordinates. At classification stage, we take advantage of AdaBoost vehicle detector and calibration’s result cooperate with edge complexity and width and height ratio to do classification. Each stage of system has its main functionality and can perform well when they are combined together. This paper demonstrates a robust system for vehicles, pedestrians, and objects classification which can operate well and can be applied to real-time applications. This paper also presents a robust vehicle detection system which can detect various kinds of back-viewed vehicles in a scene.
To further improve the performance of our system, some enhancements or trials can be made in the future, such as the performance might be further reinforced by adding auxiliary features. In addition, the proposed system can be developed to auto-calibration system, like including line-detection. Finally, the experimental results show the opportunity of classifying vehicles, like sedan, truck and bus or classifying pedestrians and motorcycles.
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