Pedestrian dynamics modeling and social force analysis based on object detection
IntroductionObject detection is a fundamental component of modern computational applications, playing a crucial role in pedestrian analysis, autonomous navigation, and crowd monitoring. Despite its widespread utility, pedestrian-oriented object detection faces significant challenges, including dynam...
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| Published in: | Frontiers in physics Vol. 13 |
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07.07.2025
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| Abstract | IntroductionObject detection is a fundamental component of modern computational applications, playing a crucial role in pedestrian analysis, autonomous navigation, and crowd monitoring. Despite its widespread utility, pedestrian-oriented object detection faces significant challenges, including dynamic crowd behaviors, occlusions, multi-scale variability, and complex urban environments, which hinder the accuracy and robustness of existing models.MethodsTo address these challenges, we propose a novel framework that integrates the Information-Geometric Variational Inference Framework (IGVIF) with the Adaptive Exploration-Exploitation Trade-off Strategy (AEETS), specifically tailored for pedestrian dynamics. IGVIF formulates pedestrian detection as a probabilistic inference problem, leveraging principles from information geometry to efficiently explore high-dimensional parameter spaces. By incorporating techniques such as Riemannian optimization and multi-scale parameterization, IGVIF effectively captures the hierarchical and multi-modal structures inherent in pedestrian movement patterns. AEETS dynamically balances global exploration with local refinement using entropy-based metrics and feedback-driven adjustments, allowing the system to adaptively optimize complex loss landscapes with greater precision in pedestrian scenarios.ResultsTogether, these components create a robust and adaptive framework that overcomes traditional limitations by efficiently handling large-scale pedestrian variability and densely populated environments. Experimental evaluations across multiple real-world pedestrian datasets demonstrate the superiority of our physics-inspired approach, achieving state-of-the-art performance in pedestrian detection and movement analysis.DiscussionThis work highlights the transformative potential of interdisciplinary strategies in advancing pedestrian-aware object detection, bridging computational physics with deep learning methodologies to enhance urban mobility and crowd safety. |
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| AbstractList | IntroductionObject detection is a fundamental component of modern computational applications, playing a crucial role in pedestrian analysis, autonomous navigation, and crowd monitoring. Despite its widespread utility, pedestrian-oriented object detection faces significant challenges, including dynamic crowd behaviors, occlusions, multi-scale variability, and complex urban environments, which hinder the accuracy and robustness of existing models.MethodsTo address these challenges, we propose a novel framework that integrates the Information-Geometric Variational Inference Framework (IGVIF) with the Adaptive Exploration-Exploitation Trade-off Strategy (AEETS), specifically tailored for pedestrian dynamics. IGVIF formulates pedestrian detection as a probabilistic inference problem, leveraging principles from information geometry to efficiently explore high-dimensional parameter spaces. By incorporating techniques such as Riemannian optimization and multi-scale parameterization, IGVIF effectively captures the hierarchical and multi-modal structures inherent in pedestrian movement patterns. AEETS dynamically balances global exploration with local refinement using entropy-based metrics and feedback-driven adjustments, allowing the system to adaptively optimize complex loss landscapes with greater precision in pedestrian scenarios.ResultsTogether, these components create a robust and adaptive framework that overcomes traditional limitations by efficiently handling large-scale pedestrian variability and densely populated environments. Experimental evaluations across multiple real-world pedestrian datasets demonstrate the superiority of our physics-inspired approach, achieving state-of-the-art performance in pedestrian detection and movement analysis.DiscussionThis work highlights the transformative potential of interdisciplinary strategies in advancing pedestrian-aware object detection, bridging computational physics with deep learning methodologies to enhance urban mobility and crowd safety. |
| Author | Yang, Fangyan Zhu, Daoyu Liu, Rong |
| Author_xml | – sequence: 1 givenname: Fangyan surname: Yang fullname: Yang, Fangyan – sequence: 2 givenname: Rong surname: Liu fullname: Liu, Rong – sequence: 3 givenname: Daoyu surname: Zhu fullname: Zhu, Daoyu |
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| Cites_doi | 10.1016/j.patcog.2020.107404 10.1186/s12879-023-08795-8 10.1109/tgrs.2023.3348555 10.1109/ICCV48922.2021.00349 10.1371/journal.pone.0024391 10.1109/ICCV48922.2021.00350 10.1016/j.asoc.2024.112536 10.1109/ICCV48922.2021.00305 10.1103/physreve.102.052314 10.1109/tpami.2021.3085766 10.3389/fphy.2025.1522172 10.1109/CVPR46437.2021.00577 10.1109/cvpr52688.2022.00571 10.1016/j.jag.2024.104208 10.1016/j.bspc.2023.105941 10.3389/fphy.2024.1508465 10.1016/j.jvoice.2024.09.002 10.1109/CVPR46437.2021.00281 10.3389/fphy.2024.1529899 10.1007/978-3-031-20077-9_17 10.1109/ICCVW54120.2021.00107 10.1016/j.ipm.2023.103584 10.1007/978-3-030-58452-8_13 10.18127/j00338486-202109-11 10.1109/ICCVW54120.2021.00312 10.1016/j.physa.2009.03.023 10.1109/ICCV48922.2021.00294 10.1007/978-3-031-19812-0_31 10.3390/electronics12102323 10.3390/s23167190 10.1016/j.inpa.2023.02.007 10.1109/ICCV48922.2021.00290 |
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| References | Gu (B19) 2021 Lou (B14) 2023; 12 Zhu (B2) 2020 Dmitriev (B32) 2025; 12 Zhang (B44) 2024 Sheng (B28) 2025; 13 Chen (B40) 2024; 134 Luo (B36) 2025; 12 Zhang (B41) 2025 Guan (B46) 2024; 61 Lv (B4) 2023 Wang (B22) 2021 Feng (B33) 2021 Li (B8) 2022 Misra (B26) 2021 Ishwerlal (B37) 2024; 91 Wang (B15) 2023 Xu (B21) 2021 Liu (B34) 2021 Sun (B23) 2021 Han (B29) 2021 Fu (B42) 2024; 11 Wang (B16) 2021 Liu (B17) 2021 Liu (B3) 2023 Liu (B12) 2022 Xie (B20) 2021 Bai (B11) 2022 Qin (B18) 2020; 106 Mao (B38) 2024; 169 Ng (B43) 2024; 24 Joseph (B24) 2021 Zhou (B31) 2011; 6 Zhang (B7) 2022 Fan (B25) 2021; 44 Yin (B6) 2020 Chen (B39) 2024; 62 Virasova (B5) 2021 Carion (B1) 2020 Zhu (B9) 2021 Zhou (B27) 2020; 102 Liu (B13) 2022 Reading (B30) 2021 Li (B10) 2022 Cai (B45) 2024 Zhou (B35) 2009; 388 |
| References_xml | – volume: 106 start-page: 107404 year: 2020 ident: B18 article-title: U2-net: going deeper with nested u-structure for salient object detection publication-title: Pattern Recognition doi: 10.1016/j.patcog.2020.107404 – volume: 24 start-page: 123 year: 2024 ident: B43 article-title: Study protocol: infectious diseases consortium (i3d) for study on integrated and innovative approaches for management of respiratory infections: respiratory infections research and outcome study (respiro) publication-title: BMC Infect Dis doi: 10.1186/s12879-023-08795-8 – volume: 62 start-page: 1 year: 2024 ident: B39 article-title: Dtssnet: dynamic training sample selection network for uav object detection publication-title: IEEE Trans Geosci Remote Sensing doi: 10.1109/tgrs.2023.3348555 – volume-title: IEEE international conference on computer vision year: 2021 ident: B33 article-title: Tood: task-aligned one-stage object detection doi: 10.1109/ICCV48922.2021.00349 – volume: 6 start-page: e24391 year: 2011 ident: B31 article-title: Investment strategies used as spectroscopy of financial markets reveal new stylized facts publication-title: PloS one doi: 10.1371/journal.pone.0024391 – volume-title: Detrs beat yolos on real-time object detection year: 2023 ident: B4 – start-page: 1 volume-title: 2024 IEEE international joint Conference on biometrics (IJCB) year: 2024 ident: B44 article-title: Blip-adapter: bridging vision-language models with adapters for generalizable face anti-spoofing – volume-title: IEEE international conference on computer vision year: 2021 ident: B20 article-title: Oriented r-cnn for object detection doi: 10.1109/ICCV48922.2021.00350 – volume: 169 start-page: 112536 year: 2024 ident: B38 article-title: Medical supervised masked autoencoder: crafting a better masking strategy and efficient fine-tuning schedule for medical image classification publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2024.112536 – volume-title: Transfusion: robust lidar-camera fusion for 3d object detection with transformers year: 2022 ident: B11 – volume-title: IEEE international conference on computer vision year: 2021 ident: B21 article-title: End-to-end semi-supervised object detection with soft teacher doi: 10.1109/ICCV48922.2021.00305 – volume-title: European conference on computer vision year: 2023 ident: B3 article-title: Grounding dino: marrying dino with grounded pre-training for open-set object detection – volume: 102 start-page: 052314 year: 2020 ident: B27 article-title: Predicting highway freight transportation networks using radiation models publication-title: Phys Rev E doi: 10.1103/physreve.102.052314 – volume: 44 start-page: 6024 year: 2021 ident: B25 article-title: Concealed object detection publication-title: IEEE Trans Pattern Anal Machine Intelligence doi: 10.1109/tpami.2021.3085766 – volume: 13 start-page: 1522172 year: 2025 ident: B28 article-title: Research on the integration of mems and reliable transmission of deep space networks based on time-sensitive networking publication-title: Front Phys doi: 10.3389/fphy.2025.1522172 – volume-title: Towards open world object detection year: 2021 ident: B24 doi: 10.1109/CVPR46437.2021.00577 – volume-title: International conference on learning representations year: 2020 ident: B2 article-title: Deformable detr: deformable transformers for end-to-end object detection – volume-title: International conference on learning representations year: 2022 ident: B7 article-title: Dino: detr with improved denoising anchor boxes for end-to-end object detection – start-page: 5792 year: 2022 ident: B13 article-title: Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection publication-title: Computer Vis Pattern Recognition doi: 10.1109/cvpr52688.2022.00571 – volume: 134 start-page: 104208 year: 2024 ident: B40 article-title: Object detection in aerial images using dota dataset: a survey publication-title: Int J Appl Earth Observation Geoinformation doi: 10.1016/j.jag.2024.104208 – volume-title: AAAI conference on artificial intelligence year: 2022 ident: B8 article-title: Bevdepth: acquisition of reliable depth for multi-view 3d object detection – volume: 91 start-page: 105941 year: 2024 ident: B37 article-title: Lung disease classification using chest x ray image: an optimal ensemble of classification with hybrid training publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2023.105941 – volume-title: Conference on robot learning year: 2021 ident: B16 article-title: Detr3d: 3d object detection from multi-view images via 3d-to-2d queries – volume: 12 start-page: 1508465 year: 2025 ident: B32 article-title: Self-organization of the stock exchange to the edge of a phase transition: empirical and theoretical studies publication-title: Front Phys doi: 10.3389/fphy.2024.1508465 – year: 2024 ident: B45 article-title: Voice disorder classification using wav2vec 2.0 feature extraction publication-title: J Voice doi: 10.1016/j.jvoice.2024.09.002 – volume-title: Center-based 3d object detection and tracking year: 2020 ident: B6 – volume-title: Computer vision and pattern recognition year: 2021 ident: B29 article-title: Redet: a rotation-equivariant detector for aerial object detection doi: 10.1109/CVPR46437.2021.00281 – volume: 12 start-page: 1529899 year: 2025 ident: B36 article-title: Application of mems technology in anti-electromagnetic radiation maternity clothes: state of the art and future perspectives publication-title: Front Phys doi: 10.3389/fphy.2024.1529899 – year: 2021 ident: B30 article-title: Categorical depth distribution network for monocular 3d object detection publication-title: Computer Vis Pattern Recognition – volume-title: European conference on computer vision year: 2022 ident: B10 article-title: Exploring plain vision transformer backbones for object detection doi: 10.1007/978-3-031-20077-9_17 – volume-title: Fsce: few-shot object detection via contrastive proposal encoding year: 2021 ident: B23 – start-page: 310 volume-title: European conference on computer vision year: 2025 ident: B41 article-title: Long-clip: unlocking the long-text capability of clip – volume-title: 2021 IEEE/CVF international conference on computer vision workshops (ICCVW) year: 2021 ident: B22 article-title: Fcos3d: fully convolutional one-stage monocular 3d object detection doi: 10.1109/ICCVW54120.2021.00107 – volume: 61 start-page: 103584 year: 2024 ident: B46 article-title: A t5-based interpretable reading comprehension model with more accurate evidence training publication-title: Inf Process & Management doi: 10.1016/j.ipm.2023.103584 – volume-title: European conference on computer vision year: 2020 ident: B1 article-title: End-to-end object detection with transformers doi: 10.1007/978-3-030-58452-8_13 – start-page: 115 year: 2021 ident: B5 article-title: Rich feature hierarchies for accurate object detection and semantic segmentation publication-title: Radioengineering doi: 10.18127/j00338486-202109-11 – volume-title: 2021 IEEE/CVF international conference on computer vision workshops (ICCVW) year: 2021 ident: B9 article-title: Tph-yolov5: improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios doi: 10.1109/ICCVW54120.2021.00312 – volume: 388 start-page: 2623 year: 2009 ident: B35 article-title: Numerical investigations of discrete scale invariance in fractals and multifractal measures publication-title: Physica A: Stat Mech its Appl doi: 10.1016/j.physa.2009.03.023 – volume-title: IEEE international conference on computer vision year: 2021 ident: B34 article-title: Group-free 3d object detection via transformers doi: 10.1109/ICCV48922.2021.00294 – volume-title: European conference on computer vision year: 2022 ident: B12 article-title: Petr: position embedding transformation for multi-view 3d object detection doi: 10.1007/978-3-031-19812-0_31 – volume: 12 start-page: 2323 year: 2023 ident: B14 article-title: Dc-yolov8: small-size object detection algorithm based on camera sensor publication-title: Electronics doi: 10.3390/electronics12102323 – volume-title: International conference on learning representations year: 2021 ident: B17 article-title: Unbiased teacher for semi-supervised object detection – volume-title: Italian national conference on sensors year: 2023 ident: B15 article-title: Uav-yolov8: a small-object-detection model based on improved yolov8 for uav aerial photography scenarios doi: 10.3390/s23167190 – volume-title: International conference on learning representations year: 2021 ident: B19 article-title: Open-vocabulary object detection via vision and language knowledge distillation – volume: 11 start-page: 249 year: 2024 ident: B42 article-title: Crop pest image recognition based on the improved vit method publication-title: Inf Process Agric doi: 10.1016/j.inpa.2023.02.007 – volume-title: IEEE international conference on computer vision year: 2021 ident: B26 article-title: An end-to-end transformer model for 3d object detection doi: 10.1109/ICCV48922.2021.00290 |
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| Title | Pedestrian dynamics modeling and social force analysis based on object detection |
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