TY - GEN
T1 - CDT
T2 - 14th European Conference on Computer Vision, ECCV 2016
AU - Kim, Han Ul
AU - Kim, Chang-Su
N1 - Funding Information:
This work was supported partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF- 2015R1A2A1 A10055037), and partly by the MSIP, Korea, under the ITRC support program supervised by the Institute for Information & communications Technology Promotion (No. IITP-2016-R2720-16-0007).
Publisher Copyright:
© Springer International Publishing AG 2016.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - A cooperative detection and model-free tracking algorithm, referred to as CDT, for multiple object tracking is proposed in this work. The proposed CDT algorithm has three components: object detector, forward tracker, and backward tracker. First, the object detector detects targets with high confidence levels only to reduce spurious detection and achieve a high precision rate. Then, each detected target is traced by the forward tracker and then by the backward tracker to restore undetected states. In the tracking processes, the object detector cooperates with the trackers to handle appearing or disappearing targets and to refine inaccurate state estimates. With this detection guidance, the model-free tracking can trace multiple objects reliably and accurately. Experimental results show that the proposed CDT algorithm provides excellent performance on a recent benchmark. Furthermore, an online version of the proposed algorithm also excels in the benchmark.
AB - A cooperative detection and model-free tracking algorithm, referred to as CDT, for multiple object tracking is proposed in this work. The proposed CDT algorithm has three components: object detector, forward tracker, and backward tracker. First, the object detector detects targets with high confidence levels only to reduce spurious detection and achieve a high precision rate. Then, each detected target is traced by the forward tracker and then by the backward tracker to restore undetected states. In the tracking processes, the object detector cooperates with the trackers to handle appearing or disappearing targets and to refine inaccurate state estimates. With this detection guidance, the model-free tracking can trace multiple objects reliably and accurately. Experimental results show that the proposed CDT algorithm provides excellent performance on a recent benchmark. Furthermore, an online version of the proposed algorithm also excels in the benchmark.
KW - Joint detection and tracking
KW - Model-free tracking
KW - Multiple object tracking
KW - Object detection
KW - Online multi-object tracking
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U2 - 10.1007/978-3-319-46466-4_51
DO - 10.1007/978-3-319-46466-4_51
M3 - Conference contribution
AN - SCOPUS:84990050217
SN - 9783319464657
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 851
EP - 867
BT - Computer Vision - 14th European Conference, ECCV 2016, Proceedings
A2 - Leibe, Bastian
A2 - Matas, Jiri
A2 - Sebe, Nicu
A2 - Welling, Max
PB - Springer Verlag
Y2 - 8 October 2016 through 16 October 2016
ER -