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Traffic sign detection based on visual co-saliency in complex scenes

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Abstract

Co-saliency detection aims at finding the salient regions from multiple images which capture the focus of human visual system. In this paper, a novel visual co-saliency algorithm is proposed, which adopts three human visual attention cues: contrast, center-bias and symmetry. In order to apply co-saliency to the detection of traffic signs, a traffic sign detection framework based on visual co-saliency in complex scenes is devised. The detection process involves two stages. In the first stage, a cluster-based co-saliency model is built to generate the final co-saliency map. In the second stage, a geometric structure constraint model is constructed to discriminate the detected salient objects and then accurately achieve location of traffic signs. The advantage of our approach lies in the integration of bottom-up and top-down visual processing, and no heavy learning tasks. Experiments on a variety of benchmark databases illustrate high precision, high recall and operation efficiency of the proposed approach. Besides, for traffic sign detection it overcomes the interference of complex urbanization backgrounds. Furthermore, the best trade-off between precision and recall on warning signs is achieved, reaching 93.30% and 89.06%, respectively.

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Acknowledgements

This research was funded by State Key Laboratory of Mechanical Transmissions of Chongqing University (SKLMT-KFKT-201602), State Key Laboratory of Robotics and System (HIT)(SKLRS-2017-KF-13), Major Projects of Science and Technology in Hunan (Grant No.2017GK1010), National Key Research and Development Plan (Grant No. 2018YFB1201602), National Natural Science Foundation of China (Grant No.61403426), Fundamental Research Funds for the Central Universities of Central South University (Grant No.2017zzts490) and National Natural Science Foundation of Hunan (Grant No.2018JJ2531, 2018JJ2197).

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, China

    Lingli Yu & Xumei Xia

  2. The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China

    Lingli Yu

  3. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Haerbin, China

    Lingli Yu

  4. School of Computer and Information Engineering, Hunan University of Commerce, Changsha, China

    Kaijun Zhou

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  1. Lingli Yu
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  2. Xumei Xia
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Correspondence to Xumei Xia or Kaijun Zhou.

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Cite this article

Yu, L., Xia, X. & Zhou, K. Traffic sign detection based on visual co-saliency in complex scenes. Appl Intell 49, 764–790 (2019). https://doi.org/10.1007/s10489-018-1298-8

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