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; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 273-282

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.