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Vehicle detection based on visual attention mechanism and adaboost cascade classifier in intelligent transportation systems

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Abstract

Robust and efficient vehicle detection is an essential task in intelligent transportation systems (ITS). Unfortunately, due to a great diversity of vehicle profiles and outdoor illumination conditions, it is a challenge to detect vehicles effectively. This paper proposes a method for high-performance vehicle detection based on visual attention mechanism and AdaBoost cascade classifier. Our method constructs the structural Haar features and extracts the features of samples using structural Haar features and trains an AdaBoost cascade classifier. Then we use the visual attention mechanism to extract the target candidate region. At last, we generate detecting sub-windows in the candidate region and discriminate them with the cascade classifier to realize vehicle detection. We compare the performance of this method against two variants, one using MB–LBP features and another using Haar features. The experimental results demonstrate satisfactory performance for the proposed method in term of training speed, detecting speed and detecting accuracy.

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Acknowledgements

This work is sponsored by Qing Lan Project of Jiangsu Province-China, the Fundamental Research Funds for the Central Universities-China (Grant No. 30916011206) and the Six Talent Peaks Project in Jiangsu Province-China (Grant No. 2015-XCL-008).

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Authors and Affiliations

  1. Department of Optoelectronic Technology, School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Xu Chen, Lei Liu, Yubin Deng & Xiangyu Kong

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  1. Xu Chen
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  2. Lei Liu
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  3. Yubin Deng
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  4. Xiangyu Kong
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Correspondence to Lei Liu.

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Chen, X., Liu, L., Deng, Y. et al. Vehicle detection based on visual attention mechanism and adaboost cascade classifier in intelligent transportation systems. Opt Quant Electron 51, 263 (2019). https://doi.org/10.1007/s11082-019-1977-7

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