TY - JOUR
T1 - Multi-object tracking with an adaptive generalized labeled multi-Bernoulli filter
AU - Do, Cong Thanh
AU - Dat Nguyen, Tran Thien
AU - Moratuwage, Diluka
AU - Shim, Changbeom
AU - Chung, Yon Dohn
N1 - Funding Information:
This work was partly supported by the Joint-scholarship between Ministry of Education and Training Vietnam and Curtin International Postgraduate Research Scholarship (MOET-CIPRS), and the Ministry of Science and ICT (MSIT), Korea, under the ICT Creative Consilience program (IITP-2021-2020-0-01819) supervised by the Institute for Information & communications Technology Planning & Evaluation (IITP).
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/7
Y1 - 2022/7
N2 - The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process. Further, the information on locations where the objects appear, their detection probabilities, and the statistics of the sensor's false alarms significantly influence the tracking accuracy of the filter. However, this information is usually assumed to be known and provided by the users. In this paper, we propose an adaptive generalized labeled multi-Bernoulli (GLMB) filter which can track multiple objects without prior knowledge of the aforementioned information. Experimental results show that the performance of the proposed filter is comparable to an ideal GLMB filter supplied with correct information of the tracking scenarios.
AB - The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process. Further, the information on locations where the objects appear, their detection probabilities, and the statistics of the sensor's false alarms significantly influence the tracking accuracy of the filter. However, this information is usually assumed to be known and provided by the users. In this paper, we propose an adaptive generalized labeled multi-Bernoulli (GLMB) filter which can track multiple objects without prior knowledge of the aforementioned information. Experimental results show that the performance of the proposed filter is comparable to an ideal GLMB filter supplied with correct information of the tracking scenarios.
KW - Adaptive birth model
KW - Bootstrapping
KW - GLMB Filter
KW - Multi-object Bayes filter
KW - Unknown clutter rate
KW - Unknown detection probability
UR - http://www.scopus.com/inward/record.url?scp=85126012764&partnerID=8YFLogxK
U2 - 10.1016/j.sigpro.2022.108532
DO - 10.1016/j.sigpro.2022.108532
M3 - Article
AN - SCOPUS:85126012764
SN - 0165-1684
VL - 196
JO - Signal Processing
JF - Signal Processing
M1 - 108532
ER -