Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video (CVPR 2023)

Wenzheng Zeng¹, Yang Xiao^1†, Sicheng Wei¹, Jinfang Gan¹, Xintao Zhang¹, Zhiguo Cao¹, Zhiwen Fang², Joey Tianyi Zhou³.

¹Huazhong University of Science and Technology, ²Southern Medical University, ³A*STAR.

Project | Paper | arXiv | Video | Poster | Dataset

This repository contains the official implementation of the CVPR 2023 paper "Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video".

Highlights

• New Task: It is the first time that the task of instance-level multi-person eyeblink detection in untrimmed videos is formally defined and explored. We think that a good multi-person eyeblink detection algorithm should be able to (1) detect and track human instances’ faces reliably to ensure the instance-level analysis ability along the whole video, and (2) detect eyeblink boundaries accurately within each human instance to ensure the precise awareness of their eyeblink behaviors. We design new metrics to give attention to both instance awareness quality and eyeblink detection quality;
• New Dataset: To support this research task, we introduce MPEblink. It is featured with multi-instance, unconstrained, and untrimmed, which makes it more challenging and offers a closer correspondence to real-world demands;
• New Framework: We propose a one-stage multi-person eyeblink detection method InstBlink. It can jointly perform face detection, tracking, and instance-level eyeblink detection. Such a task-joint paradigm can benefit the sub-tasks uniformly. Benefited from the one-stage design, InstBlink also shows high efficiency especially in multi-instance scenarios.

Implementation of InstBlink

Installation

1. Create a new conda environment:

   conda create -n instblink python=3.9
   conda activate instblink

2. Install Pytorch (1.7.1 is recommended), scipy, tqdm, pandas.

3. Install MMDetection.

   • Install MMCV first. 1.4.8 is recommended.

   • cd MPEblink
     pip install -v -e .

Data Preparation

1. Download the MPEblink dataset. Remember to change the dataset root path into yours in configs/base/mpeblink.py.

2. Convert the videos to raw frames.

   python tools/dataset_converters/mpeblink_build_raw_frames_dataset.py --root $YOUR_DATA_PATH

Demo

You can put some videos in demo_video/source_video/ and get the visualization inference result in demo_video/visual_result/ by running the following command:

bash tools/code_for_demo/demo.sh

Inference & Evaluation

• You can download the pre-trained model at Google Drive or Baidu Drive (code avk9) and put it in the pretrained_models directory.

• Run test.sh for inference and evaluation. Remember to change the dataset path into yours.

  bash tools/test_eval.sh

Training

• Download the pretrained tevit_r50 model and place it in the pretrained_models directory.

• Run train.sh to begin training.

  bash tools/train.sh

Acknowledgement

This code is inspired by TeViT and MMDetection. Thanks for their great contributions on the computer vision community.

Citation

If you find our work useful in your research, please consider to cite our paper:

@inproceedings{zeng2023real,
  title={Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video},
  author={Zeng, Wenzheng and Xiao, Yang and Wei, Sicheng and Gan, Jinfang and Zhang, Xintao and Cao, Zhiguo and Fang, Zhiwen and Zhou, Joey Tianyi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={13854--13863},
  year={2023}
}