80 lines
4.5 KiB
TeX
80 lines
4.5 KiB
TeX
\relax
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\citation{MultiCameraReview,DeepLearningMultiCam}
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\@writefile{toc}{\contentsline {title}{Anomalous Crowd Gathering Prediction Method Based on Spatial-Temporal Graph Convolutional Network in Multi-Camera Surveillance Systems}{1}{}\protected@file@percent }
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\@writefile{toc}{\authcount {1}}
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\@writefile{toc}{\contentsline {author}{No Author Given}{1}{}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction.}{1}{}\protected@file@percent }
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\citation{MultiCameraReview}
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\citation{DeepLearningMultiCam}
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\citation{TopologyAwareMCN,LearningSpatialRelations}
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\citation{SaturationSuppression,NonlinearWeightingAnomaly}
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\citation{STGCNTraffic}
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\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Temporal evolution of abnormal aggregation density over 12 consecutive frames. Each node represents a surveillance camera in the simulated urban network, and the edge indicates physical connectivity or proximity between cameras. The color intensity of each node reflects the computed abnormal aggregation degree at that time step. Darker nodes indicate higher levels of abnormal crowd gathering. This visualization illustrates how potential anomaly hotspots evolve over time and migrate through the camera network. }}{2}{}\protected@file@percent }
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\newlabel{fig:aggregation-sequence}{{1}{2}{}{figure.1}{}}
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\citation{TopologyAwareMCN}
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\citation{LearningSpatialRelations}
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\citation{GNNReview}
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\citation{KipfWelling}
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\@writefile{toc}{\contentsline {section}{\numberline {2}Related Works.}{3}{}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Camera Topology Diagram.}{3}{}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}Graph Convolutional Neural Network.}{3}{}\protected@file@percent }
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\citation{GG,DD,DG,MS,IG,MD,MO}
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\citation{GAT}
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\citation{GraphSAGE}
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\citation{AT,AG,AGL,AH}
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\citation{STGCNTraffic}
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\citation{TimeGNN}
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\citation{StemGNN}
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\citation{DyGraphformer}
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\citation{H-STGCN}
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\citation{STS-GCN}
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\citation{GCNInformer}
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\citation{GRAST-Frost}
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\citation{Stagcn}
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}The Combination of Time Series Prediction and Graph Neural Networks.}{4}{}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.1} Spatial-Temporal Graph Convolutional Network.}{5}{}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Anomaly Aggregation Degree.}{6}{}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{\numberline {4}Experiment.}{9}{}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces The simulation visualization interface for crowd aggregation; grey areas represent roads, red dots indicate abnormal gathering crowds, and blue dots represent normal pedestrians. The larger red markers are the destinations of the gatherings. }}{10}{}\protected@file@percent }
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\newlabel{fig:aggregation-sequence}{{2}{10}{}{figure.2}{}}
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\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Incidental Crowd Scenario Hit Rate Comparison}}{11}{}\protected@file@percent }
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\newlabel{tab:incidental_hit_rate}{{1}{11}{}{table.1}{}}
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\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Demonstration Scenario Hit Rate Comparison}}{11}{}\protected@file@percent }
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\newlabel{tab:demonstration_hit_rate}{{2}{11}{}{table.2}{}}
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\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Urban Riot Scenario Hit Rate Comparison}}{11}{}\protected@file@percent }
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\newlabel{tab:riot_hit_rate}{{3}{11}{}{table.3}{}}
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\@writefile{toc}{\contentsline {section}{\numberline {5} Conclusion. }{11}{}\protected@file@percent }
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\bibcite{MultiCameraReview}{1}
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\bibcite{DeepLearningMultiCam}{2}
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\bibcite{TopologyAwareMCN}{3}
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\bibcite{LearningSpatialRelations}{4}
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\bibcite{NonlinearWeightingAnomaly}{5}
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\bibcite{SaturationSuppression}{6}
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\bibcite{STGCNTraffic}{7}
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\bibcite{GNNReview}{8}
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\bibcite{GAT}{9}
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\bibcite{GraphSAGE}{10}
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\bibcite{KipfWelling}{11}
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\bibcite{TimeGNN}{12}
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\bibcite{StemGNN}{13}
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\bibcite{DyGraphformer}{14}
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\bibcite{H-STGCN}{15}
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\bibcite{STS-GCN}{16}
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\bibcite{GCNInformer}{17}
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\bibcite{GRAST-Frost}{18}
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\bibcite{Stagcn}{19}
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\bibcite{AT}{20}
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\bibcite{AG}{21}
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\bibcite{AGL}{22}
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\bibcite{AH}{23}
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\bibcite{MS}{24}
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\bibcite{GG}{25}
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\bibcite{DD}{26}
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\bibcite{IG}{27}
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\bibcite{DG}{28}
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\bibcite{MD}{29}
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\bibcite{MO}{30}
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\gdef \@abspage@last{13}
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