Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association
Abstract
We propose a framework for general multiple target tracking, where the input is a set of candidate regions in each frame, as obtained from a state of the art background learning, and the goal is to recover trajectories of targets over time from noisy observations. Due to occlusions by targets and static objects, noisy segmentation and false alarms, one foreground region may not correspond to one target faithfully. Therefore the one-to-one assumption used in most data association algorithm is not always satisfied. Our method overcomes the one-to-one assumption by formulating the visual tracking problem in terms of finding the best spatial and temporal association of observations, which maximizes the consistency of both motion and appearance of trajectories. To avoid enumerating all possible solutions, we take a data driven Markov chain Monte Carlo (DD-MCMC) approach to sample the solution space efficiently. The sampling is driven by an informed proposal scheme controlled by a joint probability model combining motion and appearance. To make sure the Markov chain to converge to a desired distribution, we propose an automatic approach to determine the parameters in the target distribution. Comparative experiments with quantitative evaluations are provided.
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Vehicle Trajectory Estimation Using Spatio-Temporal MCMC
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Tracking multiple extended objects — A Markov chain Monte Carlo approach
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Real-Time Visual Tracking and Identification for a Team of Homogeneous Humanoid Robots
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Unsupervised camera localization in crowded spaces
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Self-citationSTT: Stateful Tracking with Transformers for Autonomous Driving
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Multi-targets tracking using parallel Kalman filter
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- Authors
- Yu, Q., Medioni, G., & Cohen, I.
- Affiliations
- University of Southern California (US)
- Honeywell (United States) (US)
- ORCID
- Yu, Qian
- Venue
- 2007 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2007-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2098669323
- Cited by
- 132
- References
- 17
- Citations by year
- 2025: 1 · 2024: 7 · 2023: 5 · 2022: 3 · 2021: 4 · 2020: 5 · 2019: 4 · 2018: 5 · 2017: 8 · 2016: 12 · 2015: 7 · 2014: 7 · 2013: 5 · 2012: 8
- FWCI
- 10.94
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- Multidisciplinary University Research Initiative
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- multi-target tracking, data association, spatiotemporal association, Markov chain Monte Carlo, occlusion, false alarms, background subtraction, proposal distribution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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