Abstract
This paper addresses the problem of smoker detection in a single image. Previous smoker detection works usually focus on cigarette detection, yet often neglect the rich information of smoking behavior (especially the interaction information between smoker and cigarette). Though some attempts have been made to detect the smoking behavior, they typically rely on the temporal information in videos and are not suitable for single images. To tackle these issues, this paper proposes a novel smoker detection framework based on human-object interaction (HOI) and post-refinement. In particular, based on deep neural networks, we develop a one-stage HOI module to identify the interaction between smoker and cigarette, and exploit an additional fine-grained detector to further improve the HOI accuracy in the post-refinement module. Remarkably, we present a new benchmark dataset named SCAU smoker detection (SCAU-SD), which, to the best of our knowledge, is the first benchmark dataset for the specific task of smoker detection in single images with HOI annotations. Extensive experimental results demonstrate the superior single-image smoker detection performance of the proposed framework.
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Acknowledgement
This work was supported by the Project of Guangzhou Key Laboratory of Intelligent Agriculture (201902010081), the NSFC (61976097), and the Natural Science Foundation of Guangdong Province (2021A1515012203).
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Ling, HB., Huang, D. (2021). Single-Image Smoker Detection by Human-Object Interaction with Post-refinement. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13109. Springer, Cham. https://doi.org/10.1007/978-3-030-92270-2_16
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DOI: https://doi.org/10.1007/978-3-030-92270-2_16
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