期刊
IEEE ACCESS
卷 7, 期 -, 页码 108152-108160出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2931922
关键词
Complex scenes; moving object detection; ResNet-18; encoder-decoder network; background subtraction
资金
- National Science Foundation of China [51704115]
- Science and Technology Program of Hunan Province [2016TP1021]
- Hunan Provincial Innovation Foundation for Postgraduate [CX2018B776, CX2018B779, YCX2019A14]
- Hunan Provincial Natural Science Foundation of China [2019JJ40104]
In complex scenes, dynamic background, illumination variation, and shadow are important factors, which make conventional moving object detection algorithms suffer from poor performance. To solve this problem, a moving object detection method via ResNet-18 with encoder-decoder structure is proposed to segment moving objects from complex scenes. ResNet-18 with encoder-decoder structure possesses pixel-level classification capability to divide pixels into foreground and background, and it performs well in feature extraction because of its layers are so shallow that many more low-scale features will be retained. First, the object frames and their corresponding artificial labels are input to the network. Then, feature vectors will be generated by the encoder, and they are converted into segmentation maps by the decoder through deconvolution processing. Third, a rough matching of the moving object regions will be obtained, and finally, the Euclidean distance is used to match the moving object regions accurately. The proposed method is suitable for the scenes where dynamic background, illumination variation, and shadow exist, and experimental results on the public standard CDnet2014 and I2R datasets, from both qualitative and quantitative comparison aspects, demonstrate that the proposed method outperforms state-of-the-art algorithms significantly, and its mean F-measure increased by 1.99%similar to 29.17%.
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