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- W3042321361 abstract "The aim of this paper is pedestrian detection for campus surveillance video to achieve pedestrian flow statistics. The core of this paper mainly includes two aspects: one is that corresponding to the problem of insufficient training data set, a sample fusion strategy is proposed to build a rich and highly matched training data set; the other is that according to the characteristics of experimental data based on the YOLO v3 algorithm, the model and loss function is optimized based on YOLO v3 algorithm. Experimental results on indoor pedestrian detection show that the detection accuracy is improved, which demonstrates the effectiveness of the proposed method." @default.
- W3042321361 created "2020-07-23" @default.
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- W3042321361 date "2020-06-01" @default.
- W3042321361 modified "2023-09-27" @default.
- W3042321361 title "Indoor Pedestrian Detection Based on Deep Learning" @default.
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- W3042321361 doi "https://doi.org/10.1109/itoec49072.2020.9141638" @default.
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