TY - GEN
T1 - CDTS
T2 - 16th IEEE International Conference on Computer Vision, ICCV 2017
AU - Koh, Yeong Jun
AU - Kim, Chang-Su
N1 - Funding Information:
This work was supported partly by the National Research Foundation of Korea grant funded by the Korea government (No. NRF2015R1A2A1A10055037), and partly by The Cross-Ministry Giga KOREA Project grant funded by the Korea government (MSIT) (No. GK17P0200, Development of 4D reconstruction and dynamic deformable action model based hyper-realistic service technology).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/22
Y1 - 2017/12/22
N2 - A novel online algorithm to segment multiple objects in a video sequence is proposed in this work. We develop the collaborative detection, tracking, and segmentation (CDTS) technique to extract multiple segment tracks accurately. First, we jointly use object detector and tracker to generate multiple bounding box tracks for objects. Second, we transform each bounding box into a pixel-wise segment, by employing the alternate shrinking and expansion (ASE) segmentation. Third, we refine the segment tracks, by detecting object disappearance and reappearance cases and merging overlapping segment tracks. Experimental results show that the proposed algorithm significantly surpasses the state-of-the-art conventional algorithms on benchmark datasets.
AB - A novel online algorithm to segment multiple objects in a video sequence is proposed in this work. We develop the collaborative detection, tracking, and segmentation (CDTS) technique to extract multiple segment tracks accurately. First, we jointly use object detector and tracker to generate multiple bounding box tracks for objects. Second, we transform each bounding box into a pixel-wise segment, by employing the alternate shrinking and expansion (ASE) segmentation. Third, we refine the segment tracks, by detecting object disappearance and reappearance cases and merging overlapping segment tracks. Experimental results show that the proposed algorithm significantly surpasses the state-of-the-art conventional algorithms on benchmark datasets.
UR - http://www.scopus.com/inward/record.url?scp=85041897459&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2017.389
DO - 10.1109/ICCV.2017.389
M3 - Conference contribution
AN - SCOPUS:85041897459
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 3621
EP - 3629
BT - Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2017 through 29 October 2017
ER -