This module contains code in support of the paper An online algorithm for constrained face clustering in videos. The experiment is implemented using the MXNet framework.
In this repository, I used the original implementation of this paper from here and face recognition from insightface
The code was successfully built and run with these versions:
mxnet-cu100
cudnn 7.6.5
cudatoolkit 10.0.130
opencv 3.4.2
scikit-learn 0.20.3
Note: You can also create the environment I've tested with by importing environment.yml in conda.
In this repo, I used the 512-D embedding feature from MobileFaceNe.
usage: run_COFC_on_video.py [-h] [-vp VID_PATH] [-sd SAVE_DIR]
[-ft FEAT_THRESH] [-ot OVERLAP_THRESH]
[-st SIM_THRESH] [--model MODEL_DIR]
[--gpu GPU_ID]
optional arguments:
-h, --help show this help message and exit
-vp VID_PATH Path to the video file
-sd SAVE_DIR Directory path for saving the output
-ft FEAT_THRESH Threshold of distance bw features to belong to different
persons
-ot OVERLAP_THRESH Threshold of overlap above which two faces in
consecutive frames will belong to same track
-st SIM_THRESH Threshold of Similarity for facetracks to belong to a
cluster
--model Path to load model
--gpu The gpu id which you want to use
You can download the pretrained model here. There are more pretrained model in here
Hence, you can run the algorithm on a video file. The output directory will contain one folder corresponding to each cluster, and then in each folder it will have all the faces belonging to that cluster. The algorithm is highly sensitive to SIM_THRESH. Its value ranges between (0.0, 4.0). Increasing beyond 3.2 will create a lot of clusters, each person will be split into multiple clusters. On the other hand, keeping it below say 2.8 will create less clusters but each cluster will have faces of multiple characters.
If you found this repo useful give me a star!
@inproceedings{kulshreshtha2018online,
title={An Online Algorithm for Constrained Face Clustering in Videos},
author={Kulshreshtha, Prakhar and Guha, Tanaya},
booktitle={2018 25th IEEE International Conference on Image Processing (ICIP)},
pages={2670--2674},
year={2018},
organization={IEEE}
}
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