Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression and Semi-Supervised Domain Adaptation

Kyung Rae Kim, Yeong Jun Koh, Chang Su Kim

Research output: Contribution to journalArticle

Abstract

A novel algorithm to estimate instance-level future motion (FM) in a single image is proposed in this paper. First, the FM of an instance is defined with its direction, speed, and action classes. Then, a deep neural network, called FM-Net, is developed to determine the FM of the instance. More specifically, the multi-context pooling layer is proposed to exploit both object and global context features, and the cyclic ordinal regression scheme is developed using binary classifiers for effective FM classification. Also, the proposed FM-Net is trained in a semi-supervised domain adaptation setting to obtain reliable FM estimation results, even when a source domain in the training process and a target domain in the inference process are different. Extensive experimental results demonstrate that the proposed algorithm provides remarkable performance and thus can be used effectively for computer vision applications, including single object tracking, multiple object tracking, and crowd analysis. Furthermore, the FM dataset, collected from diverse sources and annotated manually, is released as a benchmark for single-image FM estimation.

Original languageEnglish
Article number9121271
Pages (from-to)115089-115108
Number of pages20
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • cyclic ordinal regression
  • Future motion estimation
  • semi-supervised domain adaptation

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Fingerprint Dive into the research topics of 'Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression and Semi-Supervised Domain Adaptation'. Together they form a unique fingerprint.

  • Cite this