The DOI matches its Crossref record; 23 of the 25 references it cites were found in a registry or an index.
You'll never walk alone: Modeling social behavior for multi-target tracking
Abstract
Object tracking typically relies on a dynamic model to predict the object's location from its past trajectory. In crowded scenarios a strong dynamic model is particularly important, because more accurate predictions allow for smaller search regions, which greatly simplifies data association. Traditional dynamic models predict the location for each target solely based on its own history, without taking into account the remaining scene objects. Collisions are resolved only when they happen. Such an approach ignores important aspects of human behavior: people are driven by their future destination, take into account their environment, anticipate collisions, and adjust their trajectories at an early stage in order to avoid them. In this work, we introduce a model of dynamic social behavior, inspired by models developed for crowd simulation. The model is trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera. Experiments on real sequences show that accounting for social interactions and scene knowledge improves tracking performance, especially during occlusions.
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Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Social force model for pedestrian dynamics.
Helbing, D., & Molnár, P. (1995) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.51.4282 · Cited by 7,299
A discriminatively trained, multiscale, deformable part model
Felzenszwalb, P. F., McAllester, D., & Ramanan, D. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587597 · Cited by 2,972
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
Bernardin, K., & Stiefelhagen, R. (2008) · EURASIP Journal on Image and Video Processing · doi:10.1155/2008/246309 · Cited by 2,918
Abnormal crowd behavior detection using social force model
Mehran, R., Oyama, A., & Shah, M. A. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206641 · Cited by 1,698
Lerner, A., Chrysanthou, Y., & Lischinski, D. (2007) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2007.01089.x · Cited by 1,199
A Boosted Particle Filter: Multitarget Detection and Tracking
Okuma, K., Taleghani, A., de Freitas, N., Little, J. J., & Lowe, D. G. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24670-1_3 · Cited by 1,025
Gräbner, H., & Bischof, H. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.215 · Cited by 998
Global data association for multi-object tracking using network flows
Zhang, L., Li, Y., & Nevatia, R. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587584 · Cited by 945
People-tracking-by-detection and people-detection-by-tracking
Andriluka, M., Roth, S., & Schiele, B. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587583 · Cited by 825
Wu, B., & Nevatia, R. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0027-7 · Cited by 764
A mobile vision system for robust multi-person tracking
Ess, A., Leibe, B., Schindler, K., & Van Gool, L. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587581 · Cited by 721
Self-citationRobust tracking-by-detection using a detector confidence particle filter
Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., & Gool, L. V. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459278 · Cited by 562
SPECIFICATION OF THE SOCIAL FORCE PEDESTRIAN MODEL BY EVOLUTIONARY ADJUSTMENT TO VIDEO TRACKING DATA
Johansson, A. F., Helbing, D., & Shukla, P. K. (2007) · Advances in Complex Systems · doi:10.1142/s0219525907001355 · Cited by 513
Robust Object Tracking by Hierarchical Association of Detection Responses
Huang, C., Wu, B., & Nevatia, R. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_58 · Cited by 501
Unsupervised Bayesian Detection of Independent Motion in Crowds
Brostow, G., & Cipolla, R. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.320 · Cited by 408
Floor Fields for Tracking in High Density Crowd Scenes
Ali, S., & Shah, M. A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_1 · Cited by 388
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles
Leibe, B., Konrad Schindler, B., Cornelis, N., & Gool, L. V. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.170 · Cited by 359
Self-citationTracking multiple humans in crowded environment
Zhao, T., & Nevatia, R. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315192 · Cited by 340
Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences
Antonini, G., Venegas-Martinez, S., Bierlaire, M., & Thiran, J. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-005-4797-0 · Cited by 123
A Unified Framework for Tracking through Occlusions and across Sensor Gaps
Kaucic, R., Perera, A. G. A., Brooksby, G. W., Kaufhold, J. P., & Hoogs, A. J. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.53 · Cited by 105
Large-Scale Agent-Based Pedestrian Simulation
Klügl, F., & Rindsfüser, G. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74949-3_13 · Cited by 71
Space Syntax Based Agent Simulation
Penn, A. I., & Turner, A. (2001) · Cited by 56
Flagged first, then by citations
2 of the 25 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- schadschneider, 2001, Cellular automaton approach to pedestrian dynamics—theory, PED
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Social LSTM: Human Trajectory Prediction in Crowded Spaces
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.110 · Cited by 3,675
“Finally, we demonstrate that our Social-LSTM is capable of predicting trajectories of pedestrians much more accurately than state-of-the-art methods on two publicly available datasets: ETH [49], and UCY [39].”
BackgroundMethodResultInfluentialTrajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data
Salzmann, T., Ivanovic, B., Chakravarty, P., & Pavone, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58523-5_40 · Cited by 1,250
“Our method is evaluated on three publicly-available datasets: The ETH [37], UCY [30], and nuScenes [6] datasets.”
MethodSoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H., & Savarese, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00144 · Cited by 1,137
“Traditional models for modeling and predicting human-human interactions used “social forces” to capture human motion patterns [9, 18, 31, 22, 2, 23, 21, 17].”
BackgroundMultiple object tracking: A literature review
Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., & Kim, T. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103448 · Cited by 1,271
“Employed Forces [2] repulsion, constancy, fidelity [80] repulsion, constancy, attraction, coherence [58] coherence”
BackgroundMethodDESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Lee, N., Choi, W., Vernaza, P., Choy, C., Torr, P. H. S., & Chandraker, M. K. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.233 · Cited by 1,119
“Predicting the dynamics of multiple objects is also studied in [24, 25, 3, 31], to name a few.”
Background
Metadata
- Authors
- Pellegrini, S., Ess, A., Schindler, K., & van Gool, L.
- Affiliations
- ETH Zurich (CH)
- Technische Universität Darmstadt (DE)
- Venue
- 2009 IEEE 12th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 261-268
- OpenAlex
- W2532516272
- Semantic Scholar
- 7d7a98a81715e2d3747071722b2deeed8937d122
- Cited by
- 1,683
- References
- 25
- Citations by year
- 2026: 85 · 2025: 185 · 2024: 192 · 2023: 163 · 2022: 153 · 2021: 156 · 2020: 141 · 2019: 100 · 2018: 66 · 2017: 68 · 2016: 86 · 2015: 69 · 2014: 75 · 2013: 54 · 2012: 42
- FWCI
- 24.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Lirias (KU Leuven) (submittedVersion)
- Funders
- Else Kröner-Fresenius-Stiftung
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Social Sciences › Safety Research › Evacuation and Crowd Dynamics
- Keywords
- multi-target tracking, social behavior modeling, crowd simulation, collision avoidance, occlusion handling, vehicle-mounted camera, bird's-eye view, data association, human trajectory prediction, social interaction
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