TY - GEN
T1 - Multiple moving objects tracking for automated visual surveillance
AU - Sun, Yuxiang
AU - Meng, Max Q.-H.
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2015/9/28
Y1 - 2015/9/28
N2 - Moving 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. © 2015 IEEE.
AB - Moving 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. © 2015 IEEE.
KW - Noise
UR - http://www.scopus.com/inward/record.url?scp=84959861668&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84959861668&origin=recordpage
U2 - 10.1109/ICInfA.2015.7279544
DO - 10.1109/ICInfA.2015.7279544
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781467391047
T3 - 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics
SP - 1617
EP - 1621
BT - 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics
PB - IEEE
T2 - 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics
Y2 - 8 August 2015 through 10 August 2015
ER -