Skip to content
Tukey-Inspired Video Object Segmentation
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.

TIS: Tukey-Inspired Video Object Segmentation

Contact: Brent Griffin (griffb at umich dot edu)


Tukey-Inspired Video Object Segmentation
Brent A. Griffin and Jason J. Corso
IEEE Winter Conference on Applications of Computer Vision (WACV), 2019

Please cite our paper if you find it useful for your research.

  author = {Griffin, Brent A. and Corso, Jason J.},
  booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
  title = {Tukey-Inspired Video Object Segmentation},
  year = {2019}


Video Description:


TIS processes image data to find foreground objects. The outlier scale acts as a weighting that adapts to frame-to-frame video characteristics. In this example, we focus on optical flow magnitude with outliers depicted as black pixels (middle row). Flow distributions are offset from the median (bottom row) and include the interquartile range (solid lines) and outlier thresholds (dotted lines). alt text

TIS_M processes and combines multiple segmentation masks, generating a collectively more robust method of segmentation. alt text


DAVIS results for state-of-the-art unsupervised methods. TIS-based methods achieve top results in all categories. alt text

Visual comparison of segmentation methods on DAVIS dataset. TIS_M-based methods improve performance across all categories of supervision. alt text

Pre-Computed Results

Pre-computed results for TIS_0, TIS_S, and TIS_M on DAVIS 2016 are provided in the /precomputed_results folder.

Source Code

Source code for TIS_0 and TIS_S segmentation methods from the paper is provided in the /TIS folder.

Source code for the TIS_M segmentation method from the paper is provided in the /TIS_M folder.


This code is available for non-commercial research purposes only.

You can’t perform that action at this time.