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.
|Title of host publication||Proceedings - 2019 International Conference on Computer Vision, ICCV 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|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
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Conference||17th IEEE/CVF International Conference on Computer Vision, ICCV 2019|
|Country/Territory||Korea, Republic of|
|Period||19/10/27 → 19/11/2|
Bibliographical noteFunding 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.
© 2019 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition