TY - GEN
T1 - Tracking Multiple Objects with Multimodal Dependent Measurements
T2 - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
AU - Moraffah, Bahman
AU - Brito, Cesar
AU - Venkatesh, Bindya
AU - Papandreou-Suppappola, Antonia
N1 - Funding Information:
This work was supported in part by Grant AFOSR FA9550-17-1-0100.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - We address the problem of multi-sensor multi-object tracking with unknown state parameter information. We develop algorithms capable of dealing with unknown time-dependent object and measurement cardinality and object identities. Additionally, given the dependent observations from sensors, our model takes advantage of the additional information to improve tracking performance. We robustly estimate the evolving objects and measurement cardinality with use of nonparametric modeling. In particular, we employ a dependent Dirichlet process to provide a prior on the time-varying object state distributions, and a hierarchical Dirichlet process (HDP) mixture to model measurement dependency. We demonstrate through simulations that providing multimodal dependent measurements the proposed method can accurately estimate the trajectory of objects and robustly determine the time-dependent cardinality. We also compare this method to the dependent Dirichlet process evolutionary Markov modeling (DDP-EMM) and demonstrate its improved tracking performance.
AB - We address the problem of multi-sensor multi-object tracking with unknown state parameter information. We develop algorithms capable of dealing with unknown time-dependent object and measurement cardinality and object identities. Additionally, given the dependent observations from sensors, our model takes advantage of the additional information to improve tracking performance. We robustly estimate the evolving objects and measurement cardinality with use of nonparametric modeling. In particular, we employ a dependent Dirichlet process to provide a prior on the time-varying object state distributions, and a hierarchical Dirichlet process (HDP) mixture to model measurement dependency. We demonstrate through simulations that providing multimodal dependent measurements the proposed method can accurately estimate the trajectory of objects and robustly determine the time-dependent cardinality. We also compare this method to the dependent Dirichlet process evolutionary Markov modeling (DDP-EMM) and demonstrate its improved tracking performance.
UR - http://www.scopus.com/inward/record.url?scp=85083291178&partnerID=8YFLogxK
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U2 - 10.1109/IEEECONF44664.2019.9048817
DO - 10.1109/IEEECONF44664.2019.9048817
M3 - Conference contribution
AN - SCOPUS:85083291178
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1847
EP - 1851
BT - Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
A2 - Matthews, Michael B.
PB - IEEE Computer Society
Y2 - 3 November 2019 through 6 November 2019
ER -