TY - GEN
T1 - Precise Regression for Bounding Box Correction for Improved Tracking Based on Deep Reinforcement Learning
AU - Jiang, Yifan
AU - Shin, Hyunhak
AU - Ko, Hanseok
N1 - Funding Information:
Acknowledgements: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1A2B4012720).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - In this paper, we propose a precise regression approach for correcting imprecise bounding box using deep reinforcement learning. Object tracking task essentially builds trajectory of a moving object based on detection and tracking algorithms and its current state is indicated by having the object encapsulated with a bounding box corresponding to its position and size. However due to the imperfect detection and tracking algorithms operating in complex scene, it is difficult to obtain the precise bounding box as errors frequently occur producing oversized, partial, and false bounding box, respectively. To correct the error, we train an intelligent agent that move the bounding box to the right position and scale it to its correct size matching to that of the true target. The agent is trained by deep Q-Iearning and evaluated on several state-of-the-art multiple object tracking approaches. The experimental results demonstrate that our proposed framework can effectively eliminate the object tracking bounding box error and its robustness is verified by realizing improved tracking performance in complex scene.
AB - In this paper, we propose a precise regression approach for correcting imprecise bounding box using deep reinforcement learning. Object tracking task essentially builds trajectory of a moving object based on detection and tracking algorithms and its current state is indicated by having the object encapsulated with a bounding box corresponding to its position and size. However due to the imperfect detection and tracking algorithms operating in complex scene, it is difficult to obtain the precise bounding box as errors frequently occur producing oversized, partial, and false bounding box, respectively. To correct the error, we train an intelligent agent that move the bounding box to the right position and scale it to its correct size matching to that of the true target. The agent is trained by deep Q-Iearning and evaluated on several state-of-the-art multiple object tracking approaches. The experimental results demonstrate that our proposed framework can effectively eliminate the object tracking bounding box error and its robustness is verified by realizing improved tracking performance in complex scene.
KW - Bounding box
KW - Object tracking
KW - Regression
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85054227691&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8462063
DO - 10.1109/ICASSP.2018.8462063
M3 - Conference contribution
AN - SCOPUS:85054227691
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1643
EP - 1647
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -