j50888/davis-matlab

Introduction

Package containing the Matlab implementation of the code behind: A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation DAVIS. You can find the Python implementation here.

Code Installation

Adapt the value of db_root_dir.m to point to the root dir where DAVIS is uncompressed in your system (contains folders Annotations and JPEGImages)
Run the script startup.m to add the necessary paths and perform some checks.
[If necessary] Recompile using the script build.m in case the startup script detects some files missing.

Code Usage

The script demo_sweep.m contains a demo of how the dataset images and annotations are read (all functions in db_util).
The script measures/eval_result.m runs the evaluation for the selected measures on a certain subset of the dataset.
The three measures used in the evaluation are found in the folder measures.
The folder experiments contains the scripts used to generate all plots and tables in the paper. global_table.m might be the best point ot start.

Evaluate your technique

Add your results in the folder $root_DAVIS\Results\Segmentations\480p, as the provided precomputed results, in a folder my_method
Run measures/eval_result.m on your technique: eval_result('my_method',{'J','F','T'}). (You can select which measures to use - You can skip T for fast computation)
Show your results as in experiments\global_table.m

Citation

Please cite DAVIS in your publications if it helps your research:

@inproceedings{Perazzi_CVPR_2016,
  author    = {Federico Perazzi and
               Jordi Pont-Tuset and
               Brian McWilliams and
               Luc Van Gool and
               Markus Gross and
               Alexander Sorkine-Hornung},
  title     = {A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2016}
}

Contacts

Jordi Pont-Tuset - Matlab code
Federico Perazzi - Python code

j50888 / davis-matlab