BoxMask: Revisiting Bounding Box Supervision for Video Object Detection

Official code for BoxMask: Revisiting Bounding Box Supervision for Video Object Detection, WACV 2023

Paper

Installation

Please refer to install.md for install instructions.

Getting Started

An exectuable MMTracking framework is required to run this code. Please see dataset.md and quick_run.md for the basic usage of MMTracking.

A Colab tutorial is provided. You may preview the notebook here or directly run it on Colab.

There are also usage tutorials, such as learning about configs, an example about detailed description of vid config, an example about detailed description of mot config, an example about detailed description of sot config, customizing dataset, customizing data pipeline, customizing vid model, customizing mot model, customizing sot model, customizing runtime settings and useful tools.

Video Object Detection

Supported Methods

• DFF (CVPR 2017)
• FGFA (ICCV 2017)
• SELSA (ICCV 2019)
• Temporal RoI Align (AAAI 2021)
• BoxMask+Temporal RoI Align (WACV 2023)

Supported Datasets

• ILSVRC

Acknowledgement

This codebase is heavily based on MMtracking and MMDetection. We sincerely thank their efforts for providing such useful open source frameworks

Citing BoxMask

Please cite our paper in your publications if it helps your research:

@inproceedings{hashmi2023boxmask,
  title={BoxMask: Revisiting Bounding Box Supervision for Video Object Detection},
  author={Hashmi, Khurram Azeem and Pagani, Alain and Stricker, Didier and Afzal, Muhammad Zeshan},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={2030--2040},
  year={2023}
}