Cited by 132Video Surveillance and Tracking MethodsTarget Tracking and Data Fusion in Sensor NetworksAutonomous Vehicle Technology and Safety

Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association

Yu, Qian, Medioni, Gerard, Cohen, Isaac·2007·2007 IEEE Conference on Computer Vision and Pattern Recognition

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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Crossref · 10.1109/cvpr.2007.382991

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Authors
Yu, Q., Medioni, G., & Cohen, I.
Affiliations
  • University of Southern California (US)
  • Honeywell (United States) (US)
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

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