Abstract
We explore the problem of detecting driver faces in cabs from images taken by traffic cameras. Dim light in cabs, occlusion and low resolution make it a challenging problem. We employ aggregate channel features instead of a single feature to reduce the miss rate, which will introduce more false positives. Based on the observation that most running vehicles have a license plate and the relative position between the plate and driver face has an approximately fixed pattern, we refer to the concept of deformable part-based model and regard a candidate face and a plate as two deformable parts of a face-plate couple. A candidate face will be rejected if it has a low confidence score. Experiment results demonstrate the effectiveness of our method.
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Acknowledgments
This work was supported in part by the Natural Science Foundation of China (NSFC) under Grant No. 61472038 and No. 61375044.
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Wang, Y., Xu, X., Pei, M. (2016). Driver Face Detection Based on Aggregate Channel Features and Deformable Part-Based Model in Traffic Camera. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_64
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DOI: https://doi.org/10.1007/978-3-319-46672-9_64
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