[1] SHANG Yang, SUN Xiaoliang, ZHANG Yueqiang, et al. Research on 3D target pose tracking and modeling[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(2): 60-69. [2] 张旭, 郝向阳, 李建胜, 等. 监控视频中动态目标与地理空间信息的融合与可视化方法[J]. 测绘学报, 2019, 48(11): 1415-1423. DOI: 10.11947/j.AGCS.2019.20180572. ZHANG Xu, HAO Xiangyang, LI Jiansheng, et al. Fusion and visualization method of dynamic targets in surveillance video with geospatial information[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11): 1415-1423. DOI: 10.11947/j.AGCS.2019.20180572. [3] 李德仁, 胡庆武. 基于可量测实景影像的空间信息服务[J]. 武汉大学学报(信息科学版), 2007, 32(5): 377-380, 418. LI Deren, HU Qingwu. Digital measurable image based geo-spatial information service[J]. Geomatics and Information Science of Wuhan University, 2007, 32(5): 377-380, 418. [4] CHAI Yuning, SAPP B, BANSAL M, et al. MultiPath: multiple probabilistic anchor trajectory hypotheses for behavior prediction[C]//Proceedings of 2019 Conference on Robot Learning. Osaka: Robot Learning Foundation, Inc, 2019:86-99. [5] LIANG Junwei, JIANG Lu, NIEBLES J C, et al. Peeking into the future: predicting future person activities and locations in videos[C]//Proceedings of 2019 IEEE Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 5718-5727. [6] RHINEHART N, KITANI K M, VERNAZA P. R2P2: a reparameterized pushforward policy for diverse, precise generative path forecasting[C]//Proceedings of 2018 ECCV. Cham: Springer, 2018: 794-811. [7] 李景文, 韦晶闪, 姜建武, 等. 多视角监控视频中动态目标的时空信息提取方法[J]. 测绘学报, 2022, 51(3): 388-400. DOI: 10.11947/j.AGCS.2022.20200507. LI Jingwen, WEI Jingshan, JIANG Jianwu, et al. Spatio-temporal information extraction method for dynamic targets in multi-perspective surveillance video[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(3): 388-400. DOI: 10.11947/j.AGCS.2022.20200507. [8] CHOI W, SAVARESE S. A unified framework for multi-target tracking and collective activity recognition[C]//Proceedings of 2012 European Conference on Computer Vision. Berlin: Springer, 2012: 215-230. [9] HELBING D, MOLNÁR P. Social force model for pedestrian dynamics[J]. Physical Review E, 1995, 51(5): 4282-4286. [10] ALAHI A, GOEL K, RAMANATHAN V, et al. Social LSTM: human trajectory prediction in crowded spaces[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 961-971. [11] GUPTA A, JOHNSON J, LI Feifei, et al. Social GAN: socially acceptable trajectories with generative adversarial networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 2255-2264. [12] SADEGHIAN A, KOSARAJU V, SADEGHIAN A, et al. SoPhie: an attentive GAN for predicting paths compliant to social and physical constraints[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 1349-1358. [13] 张志远, 刁英华. 结合社会特征和注意力的行人轨迹预测模型[J]. 西安电子科技大学学报, 2020, 47(1): 10-17, 79. ZHANG Zhiyuan, DIAO Yinghua. Pedestrian trajectory prediction model with social features and attention[J]. Journal of Xidian University, 2020, 47(1): 10-17, 79. [14] VEMULA A, MUELLING K, OH J. Social attention: modeling attention in human crowds[C]//Proceedings of 2018 IEEE International Conference on Robotics and Automation. Brisbane: IEEE, 2018: 4601-4607. [15] HUANG Yingfan, BI Huikun, LI Zhaoxin, et al. STGAT: modeling spatial-temporal interactions for human trajectory prediction[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE, 2019: 6271-6280. [16] YU Cunjun, MA Xiao, REN Jiawei, et al. Spatio-temporal graph transformer networks for pedestrian trajectory prediction[C]//Proceedings of 2020 European Conference on Computer Vision. Cham: Springer, 2020: 507-523. [17] OH S, HOOGS A, PERERA A, et al. AVSS 2011 demo session: a large-scale benchmark dataset for event recognition in surveillance video[C]//Proceedings of 2011 Conference on Computer Vision and Pattern Recognition. Colorado Springs: IEEE, 2011: 3153-3160. [18] LI Yuke. Which way are You going? Imitative decision learning for path forecasting in dynamic scenes[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 294-303. [19] CHANG Mingfang, LAMBERT J, SANGKLOY P, et al. Argoverse: 3D tracking and forecasting with rich maps[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 8740-8749. [20] GAIDON A, WANG Qiao, CABON Y, et al. Virtual worlds as proxy for multi-object tracking analysis[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 4340-4349. [21] WRENNINGE M, UNGER J. Synscapes: a photorealistic synthetic dataset for street scene parsing[EB/OL].[2023-08-22]. https://arxiv.org/abs/1810.08705. [22] LIANG Junwei, JIANG Lu, MURPHY K, et al. The garden of forking paths: towards multi-future trajectory prediction[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 10505-10515. [23] DOSOVITSKIY A, ROS G, CODEVILLA F, et al. CARLA: an open urban driving simulator[EB/OL].[2023-08-22]. https://arxiv.org/abs/1711.03938. [24] ZHANG Yi, WEI Xinyue, QIU Weichao, et al. RSA: randomized simulation as augmentation for robust human action recognition[EB/OL].[2023-08-22]. https://arxiv.org/abs/1912.01180. [25] LI Lihuan, PAGNUCCO M, SONG Yang. Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction[C]//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 2221-2231. [26] LIANG Junwei, JIANG Lu, HAUPTMANN A. SimAug: learning robust representations from simulation for trajectory prediction[C]//Proceedings of 2020 European Conference on Computer Vision. Cham: Springer, 2020: 275-292. [27] SHIRAZI M S, MORRIS B T. Trajectory prediction of vehicles turning at intersections using deep neural networks[J]. Machine Vision and Applications, 2019, 30(6): 1097-1109. [28] YOON Y C, KIM D Y, SONG Y M, et al. Online multiple pedestrians tracking using deep temporal appearance matching association[J]. Information Sciences, 2021, 561: 326-351. [29] LI J, CHEN C, CHENG T. Motion prediction and robust tracking of a dynamic and temporarily-occluded target by an unmanned aerial vehicle[J]. IEEE Transactions on Control Systems Technology, 2021, 29(4): 1623-1635. [30] ZHANG Hongyi, CISSE M, DAUPHIN Y N, et al. Mixup: beyond empirical risk minimization[EB/OL].[2023-08-22]. https://arxiv.org/abs/1710.09412. [31] HU Han, GU Jiayuan, ZHANG Zheng, et al. Relation networks for object detection[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3588-3597. [32] WOJKE N, BEWLEY A, PAULUS D. Simple online and realtime tracking with a deep association metric[C]//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing: IEEE, 2017: 3645-3649. [33] AWAD G, BUTT A, CURTIS K, et al. TRECVID 2018: benchmarking video activity detection, video captioning and matching, video storytelling linking and video search[EB/OL]. [2023-08-22]. http://www.tara.tcd.ie/bitstream/handle/2262/96111/tv18overview.pdf?sequence=1&isAllowed=y. [34] ZHOU Bolei, ZHAO Hang, PUIG X, et al. Scene parsing through ADE20K dataset[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 5122-5130. [35] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848. [36] JIANG Lu, ZHOU Zhengyuan, LEUNG T, et al. MentorNet: learning data-driven curriculum for very deep neural networks on corrupted labels[EB/OL].[2023-08-22]. https://arxiv.org/abs/1712.05055. [37] KURAKIN A, GOODFELLOW I J, BENGIO S. Adversarial examples in the physical world[EB/OL]. [2023-08-22].https://arxiv.org/abs/1607.02533. [38] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. [39] PELLEGRINI S, ESS A, SCHINDLER K, et al. You'll never walk alone: modeling social behavior for multi-target tracking[C]//Proceedings of 2009 IEEE International Conference on Computer Vision. Kyoto: IEEE, 2009: 261-268. [40] LERNER A, CHRYSANTHOU Y, LISCHINSKI D. Crowds by example[J]. Computer Graphics Forum, 2007, 26(3): 655-664. |