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.
@inproceedings{GrCoWACV2019,
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: https://youtu.be/FeWnFz4Cf_8
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).
TIS_M processes and combines multiple segmentation masks, generating a collectively more robust method of segmentation.
DAVIS results for state-of-the-art unsupervised methods. TIS-based methods achieve top results in all categories.
Visual comparison of segmentation methods on DAVIS dataset. TIS_M-based methods improve performance across all categories of supervision.
Pre-computed results for TIS_0, TIS_S, and TIS_M on DAVIS 2016 are provided in the /precomputed_results folder.
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.