TY - GEN
T1 - Kalman particle PHD filter for multi-target visual tracking
AU - Ma, Weizhang
AU - Ma, Bo
AU - Zhan, Xueliang
PY - 2012
Y1 - 2012
N2 - We propose a novel filtering algorithm based on the Probability Hypothesis Density (PHD) for multi-target visual tracking. Some previous methods using particle PHD filter for multi-target tracking have showed superiority in computation and achieved good results, however, the proposal distribution and observation model used in the standard particle PHD filter are naive and poor, which degrade the performance of the tracker. In this paper, the Kalman filter is applied to generate the proposal distribution, which considers the latest observations in the state transition and matches the posterior density well. Moreover, we adopt a precise observation model, which takes the dynamic state of the targets into account, as well as the appearance. The simulation results on real-world scenarios show that our method provides a robust tracking and outperforms other particle PHD filters.
AB - We propose a novel filtering algorithm based on the Probability Hypothesis Density (PHD) for multi-target visual tracking. Some previous methods using particle PHD filter for multi-target tracking have showed superiority in computation and achieved good results, however, the proposal distribution and observation model used in the standard particle PHD filter are naive and poor, which degrade the performance of the tracker. In this paper, the Kalman filter is applied to generate the proposal distribution, which considers the latest observations in the state transition and matches the posterior density well. Moreover, we adopt a precise observation model, which takes the dynamic state of the targets into account, as well as the appearance. The simulation results on real-world scenarios show that our method provides a robust tracking and outperforms other particle PHD filters.
KW - Kalman filter
KW - Particle PHD filter
KW - multi-target visual tracking
KW - observation model
KW - proposal distribution
UR - http://www.scopus.com/inward/record.url?scp=84865816178&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-31919-8_44
DO - 10.1007/978-3-642-31919-8_44
M3 - Conference contribution
AN - SCOPUS:84865816178
SN - 9783642319181
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 341
EP - 348
BT - Intelligent Science and Intelligent Data Engineering - Second Sino-Foreign-Interchange Workshop, IScIDE 2011, Revised Selected Papers
T2 - 2nd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2011
Y2 - 23 October 2011 through 25 October 2011
ER -