The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera
Open accessAbstract
In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex scenes using a monocular, potentially moving, uncalibrated camera. We propose a novel approach for multiperson tracking-by-detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online-trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. The main contribution of this paper is to explore how these unreliable information sources can be used for robust multiperson tracking. The algorithm detects and tracks a large number of dynamically moving people in complex scenes with occlusions, does not rely on background modeling, requires no camera or ground plane calibration, and only makes use of information from the past. Hence, it imposes very few restrictions and is suitable for online applications. Our experiments show that the method yields good tracking performance in a large variety of highly dynamic scenarios, such as typical surveillance videos, webcam footage, or sports sequences. We demonstrate that our algorithm outperforms other methods that rely on additional information. Furthermore, we analyze the influence of different algorithm components on the robustness.
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Cites
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Novel approach to nonlinear/non-Gaussian Bayesian state estimation
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Sequential Monte Carlo Methods in Practice
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CONDENSATION—Conditional Density Propagation for Visual Tracking
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An algorithm for tracking multiple targets
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Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
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You'll never walk alone: Modeling social behavior for multi-target tracking
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Cited by
VirtualWorlds as Proxy for Multi-object Tracking Analysis
Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.470 · Cited by 1,079
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
Wen, L., Du, D., Cai, Z., Lei, Z., Chang, M.-C., Qi, H., Lim, J., Yang, M.-H., & Lyu, S. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.102907 · Cited by 804
Multiple Hypothesis Tracking Revisited
Kim, C., Li, F., Ciptadi, A., & Rehg, J. M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.533 · Cited by 666
Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., & Yu, N. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.518 · Cited by 406
In defense of color-based model-free tracking
Possegger, H., Mauthner, T., & Bischof, H. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298823 · Cited by 370
Metadata
- Authors
- Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., & Van Gool, L.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2011-09
- Volume
- 33
- Issue
- 9
- Pages
- 1820-1833
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2148958980
- Cited by
- 579
- References
- 45
- FWCI
- 40.17
- Type
- journal article
- Language
- en
- Open access
- green
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- Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., & Van Gool, L. (2011). Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2010.232
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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