Abstract
Aiming at the problem that traditional vehicle detection methods often fail to ensure the accuracy and speed simultaneously, a vehicle detection method based on background difference and improved Gentle Adaboost classifier is proposed. Firstly, the foreground region is obtained by using the background difference method, and the morphological processing is applied properly to get better candidate foreground regions. Then, the cascaded Adaboost classifiers are used to detect multi-scale vehicles in these regions. In this paper, we adopt effective search strategy, which can greatly reduce the number of search windows, and further improve the detection speed. The experimental results show that the proposed method not only can obtain high accuracy, but also has strong real-time performance. Precisely, the highest accuracy reaches to 96.0% and the highest detection speed reaches to 51.4 FPS.
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Jiang, T., Cai, M., Zhang, Y., Zhao, X. (2017). A Fast Video Vehicle Detection Approach Based on Improved Adaboost Classifier. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_41
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DOI: https://doi.org/10.1007/978-3-319-61833-3_41
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