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IEICE TRANSACTIONS on Information and Systems. 103 ( 6 ), 1371 - 1379 . ( 2020 ). Chen C, Xiao H, Liu Y, : Dual-Task Integrated Network for Fast Pedestrian Detection in Crowded Scenes. IEICE TRANSACTIONS on Information and Systems. 103(6), 1371-1379. (2020).","journal-title":"IEICE TRANSACTIONS on Information and Systems."},{"key":"e_1_3_2_1_6_1","first-page":"637","volume-title":"the European Conference on Computer Vision.","author":"Zhang S","year":"2018","unstructured":"Zhang S , Wen L , Bian X , : Occlusion-aware R-CNN: detecting pedestrians in a crowd . In: the European Conference on Computer Vision. pp. 637 - 653 . ( 2018 ). Zhang S, Wen L, Bian X, : Occlusion-aware R-CNN: detecting pedestrians in a crowd. In: the European Conference on Computer Vision. pp. 637-653. 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Song T, Sun L, Xie D, Small-scale pedestrian detection based on topological line localization and temporal feature aggregation. In: the European Conference on Computer Vision. pp. 536-551. (2018)."},{"key":"e_1_3_2_1_18_1","first-page":"135","volume-title":"the European Conference on Computer Vision.","author":"Zhou C","year":"2018","unstructured":"Zhou C , Yuan J. : Bi-box regression for pedestrian detection and occlusion estimation . In: the European Conference on Computer Vision. pp. 135 - 151 . ( 2018 ). Zhou C, Yuan J.: Bi-box regression for pedestrian detection and occlusion estimation. In: the European Conference on Computer Vision. pp. 135-151. (2018)."},{"doi-asserted-by":"crossref","unstructured":"Viola P Jones M J. Robust real-time face detection[J]. International journal of computer vision 57(2): 137-154. (2004)  Viola P Jones M J. Robust real-time face detection[J]. International journal of computer vision 57(2): 137-154. 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