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基于Transformer和語義增強的人群計數算法
網絡安全與數據治理 2023年第5期
何晴,,楊倩倩,,彭思凡,殷保群
(中國科學技術大學信息科學技術學院,,安徽合肥230027)
摘要: 針對人群圖像中的尺度變化問題,,提出了基于Transformer和語義增強的人群計數算法,。為了能有效應對尺度變化問題,首先引入Transformer作為主干網對全局上下文進行建模來獲得全局感受野,。然后由上至下依次融合主干網相鄰層次的特征圖,,在融合過程中強化多個層次特征圖的語義信息。接著對多層次特征圖進行動態(tài)特征選擇,,選擇出適合密度圖生成的特征,。最后,通過注意力圖來調整密度圖抵抗背景干擾,,以此來生成高質量的人群密度估計圖,。在ShanghaiTech、UCFQNRF和JHUCROWD++三個數據集上進行了大量的實驗來對算法的有效性進行驗證,,實驗結果表明所提算法能有效提高模型的準確性和魯棒性,。
中圖分類號:TP391.1
文獻標識碼:A
DOI:10.19358/j.issn.2097-1788.2023.05.009
引用格式:何晴,楊倩倩,,彭思凡,等.基于Transformer和語義增強的人群計數算法[J].網絡安全與數據治理,,2023,42(5):50-58.
Transformer and semantic enhancement for crowd counting
He Qing,,Yang Qianqian,Peng Sifan,,Yin Baoqun
(School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China)
Abstract: Aiming at the problem of scale variation in crowd images, this paper proposes a crowd counting algorithm based on Transformer and semantic enhancement. Firstly, Transformer is introduced as the backbone of the network. Because it can model the global context and obtain the global receptive field, which can effectively deal with the scale variation. Then, the feature maps of adjacent levels of the backbone network are fused from top to bottom in turn, and the semantic information of multiple levels of feature maps is strengthened in the fusion process. Afterwards the dynamic feature selection of multilevel feature maps is carried out, and the features suitable for density map generation are selected. Finally, the density map is adjusted to resist background interference by attention masks, so as to generate highquality crowd density estimation map. In this paper, a large number of experiments are carried out on ShanghaiTech, UCF_QNRF and JHUCROWD++ datasets to verify the effectiveness of the algorithm. The experimental results show that the proposed algorithm can effectively improve the accuracy and robustness of the model.
Key words : crowd counting; Transformer; semantic enhancement; feature selection

0    引言

人群計數在視頻監(jiān)控,、人群分析和公共安全領域發(fā)揮著重要作用,考慮到大規(guī)模的人群聚集事件的頻繁發(fā)生,對擁擠場景的人群分析十分必要,。然而現階段人群計數的應用還受到很大的限制,,在諸多限制中,圖像中人頭尺寸不一致的問題尤其受到大多數研究者的關注,。由于攝像頭高度和角度受到限制,,所拍攝的圖像存在透視失真,從而導致了圖像中目標尺度差異較大,。如圖1所示,,離攝像頭遠處的目標尺度較大,近處的目標尺度較小,。為了解決尺度變化問題,,本文提出基于Transformer語義增強的人群計數算法,利用Transformer獲取全局感受野,,由上至下依次融合相鄰層次特征并對語義信息進行增強,,動態(tài)選擇適合密度圖生成的特征,從而生成高質量的人群密度估圖,。



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作者信息:

何晴,,楊倩倩,彭思凡,,殷保群

(中國科學技術大學信息科學技術學院,,安徽合肥230027)


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