Instance-level future motion estimation in a single image based on ordinal regression

Kyung Rae Kim; Whan Choi; Yeong Jun Koh; Seong Gyun Jeong; Chang Su Kim

doi:10.1109/ICCV.2019.00036

Instance-level future motion estimation in a single image based on ordinal regression

Kyung Rae Kim, Whan Choi, Yeong Jun Koh, Seong Gyun Jeong, Chang Su Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

Abstract

A novel algorithm to estimate instance-level future motion in a single image is proposed in this paper. We first represent the future motion of an instance with its direction, speed, and action classes. Then, we develop a deep neural network that exploits different levels of semantic information to perform the future motion estimation. For effective future motion classification, we adopt ordinal regression. Especially, we develop the cyclic ordinal regression scheme using binary classifiers. Experiments demonstrate that the proposed algorithm provides reliable performance and thus can be used effectively for vision applications, including single and multi object tracking. Furthermore, we release the future motion (FM) dataset, collected from diverse sources and annotated manually, as a benchmark for single-image future motion estimation.

Original language	English
Title of host publication	Proceedings - 2019 International Conference on Computer Vision, ICCV 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	273-282
Number of pages	10
ISBN (Electronic)	9781728148038
DOIs	https://doi.org/10.1109/ICCV.2019.00036
Publication status	Published - 2019 Oct
Event	17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of Duration: 2019 Oct 27 → 2019 Nov 2

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
Volume	2019-October
ISSN (Print)	1550-5499

Conference

Conference	17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
Country/Territory	Korea, Republic of
City	Seoul
Period	19/10/27 → 19/11/2

Bibliographical note

Funding Information:
This work was supported by ‘The Cross-Ministry Giga KOREA Project’ grant funded by the Korea government (MSIT) (No. GK18P0200, Development of 4D reconstruction and dynamic deformable action model based hyperrealistic service technology), by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2018R1A2B3003896), and by NAVER LABS.

Publisher Copyright:
© 2019 IEEE.

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICCV.2019.00036

Cite this

Kim, K. R., Choi, W., Koh, Y. J., Jeong, S. G., & Kim, C. S. (2019). Instance-level future motion estimation in a single image based on ordinal regression. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019 (pp. 273-282). Article 9009521 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2019.00036

Instance-level future motion estimation in a single image based on ordinal regression. / Kim, Kyung Rae; Choi, Whan; Koh, Yeong Jun et al.
Proceedings - 2019 International Conference on Computer Vision, ICCV 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 273-282 9009521 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2019-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, KR, Choi, W, Koh, YJ, Jeong, SG & Kim, CS 2019, Instance-level future motion estimation in a single image based on ordinal regression. in Proceedings - 2019 International Conference on Computer Vision, ICCV 2019., 9009521, Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-October, Institute of Electrical and Electronics Engineers Inc., pp. 273-282, 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea, Republic of, 19/10/27. https://doi.org/10.1109/ICCV.2019.00036

Kim KR, Choi W, Koh YJ, Jeong SG, Kim CS. Instance-level future motion estimation in a single image based on ordinal regression. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 273-282. 9009521. (Proceedings of the IEEE International Conference on Computer Vision). doi: 10.1109/ICCV.2019.00036

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title = "Instance-level future motion estimation in a single image based on ordinal regression",

abstract = "A novel algorithm to estimate instance-level future motion in a single image is proposed in this paper. We first represent the future motion of an instance with its direction, speed, and action classes. Then, we develop a deep neural network that exploits different levels of semantic information to perform the future motion estimation. For effective future motion classification, we adopt ordinal regression. Especially, we develop the cyclic ordinal regression scheme using binary classifiers. Experiments demonstrate that the proposed algorithm provides reliable performance and thus can be used effectively for vision applications, including single and multi object tracking. Furthermore, we release the future motion (FM) dataset, collected from diverse sources and annotated manually, as a benchmark for single-image future motion estimation.",

author = "Kim, {Kyung Rae} and Whan Choi and Koh, {Yeong Jun} and Jeong, {Seong Gyun} and Kim, {Chang Su}",

note = "Funding Information: This work was supported by {\textquoteleft}The Cross-Ministry Giga KOREA Project{\textquoteright} grant funded by the Korea government (MSIT) (No. GK18P0200, Development of 4D reconstruction and dynamic deformable action model based hyperrealistic service technology), by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2018R1A2B3003896), and by NAVER LABS. Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 ; Conference date: 27-10-2019 Through 02-11-2019",

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N1 - Funding Information: This work was supported by ‘The Cross-Ministry Giga KOREA Project’ grant funded by the Korea government (MSIT) (No. GK18P0200, Development of 4D reconstruction and dynamic deformable action model based hyperrealistic service technology), by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2018R1A2B3003896), and by NAVER LABS. Publisher Copyright: © 2019 IEEE.

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N2 - A novel algorithm to estimate instance-level future motion in a single image is proposed in this paper. We first represent the future motion of an instance with its direction, speed, and action classes. Then, we develop a deep neural network that exploits different levels of semantic information to perform the future motion estimation. For effective future motion classification, we adopt ordinal regression. Especially, we develop the cyclic ordinal regression scheme using binary classifiers. Experiments demonstrate that the proposed algorithm provides reliable performance and thus can be used effectively for vision applications, including single and multi object tracking. Furthermore, we release the future motion (FM) dataset, collected from diverse sources and annotated manually, as a benchmark for single-image future motion estimation.

AB - A novel algorithm to estimate instance-level future motion in a single image is proposed in this paper. We first represent the future motion of an instance with its direction, speed, and action classes. Then, we develop a deep neural network that exploits different levels of semantic information to perform the future motion estimation. For effective future motion classification, we adopt ordinal regression. Especially, we develop the cyclic ordinal regression scheme using binary classifiers. Experiments demonstrate that the proposed algorithm provides reliable performance and thus can be used effectively for vision applications, including single and multi object tracking. Furthermore, we release the future motion (FM) dataset, collected from diverse sources and annotated manually, as a benchmark for single-image future motion estimation.

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Instance-level future motion estimation in a single image based on ordinal regression

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