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- W2896747332 abstract "This paper investigates the automated recognition of smoky vehicles in surveillance videos. Most current methods recognize the smoky vehicle based on additional sensors. It is a challenging task to recognize smoky vehicle from images due to complex background and the movement of the vehicles. In this paper, we develop a spatial pyramid pooling convolutional neural network (SPPCNN) for smoky vehicle detection according to the image processing algorithm. Resizing the image will lead to shape changes of the smoke plume, and reducing the recognition rate. The spatial pyramid pooling layer is used to overcome this problem. The input of CNN is raw pictures of the tail of the vehicles which captured with method of foreground-background segmentation. This model extracts features from both the vehicles and the smoke by performing convolutions, and regarding the vehicle information as a prior. Experimental results show that this method achieved very low false alarm rates below 1% with detection rates above 80% on real video sequences." @default.
- W2896747332 created "2018-10-26" @default.
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- W2896747332 date "2018-07-01" @default.
- W2896747332 modified "2023-10-11" @default.
- W2896747332 title "A Spatial Pyramid Pooling Convolutional Neural Network for Smoky Vehicle Detection" @default.
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- W2896747332 doi "https://doi.org/10.23919/chicc.2018.8483521" @default.
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