Multiple Moving Objects Tracking for Automated Visual Surveillance
Refereed conference paper presented and published in conference proceedings


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AbstractMoving objects tracking is of great significance for automated visual surveillance. Conventional tracking algorithms, such as Kalman filter or particle filter, have shown the effectiveness and robustness in many practical applications. However, the Bayesian filter is not designed for tacking multiple moving objects. The difficulty is the data association between the measurements and the tracks. Tracking can fail due to the confusion of similar measurements from adjacent moving objects. This paper proposes an approach for multiple moving objects tracking. We formulate the measurement assignment process as a problem of finding the matching with the maximum weight in a bipartite graph. Moving objects are detected by background subtraction. We test our approach using public datasets. The experimental results demonstrate that our approach is able to track multiple moving objects correctly.
All Author(s) ListSun YX, Meng MQH
Name of ConferenceIEEE International Conference on Information and Automation 2015
Start Date of Conference08/08/2015
End Date of Conference10/08/2015
Place of ConferenceLijiang
Country/Region of ConferenceChina
Detailed descriptionorganized by IEEE,
Year2015
Month1
Day1
PublisherIEEE
Pages1617 - 1621
eISBN978-1-4673-9104-7
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesAutomation & Control Systems; Engineering; Engineering, Electrical & Electronic

Last updated on 2020-17-10 at 00:48