/person-search-PPCC

person search by progressive propagation via competitive consensus

Primary LanguagePython

Person Search by Progressive Propagation via Competitive Consensus (PPCC)

This is the implement of our ECCV 2018 paper

Person Search in Videos with One Portrait Through Visual and Temporal Links.
Qingqiu Huang, Wentao Liu, Dahua Lin. ECCV 2018, Munich.

This project is based on our person search dataset -- Cast Search in Movies (CSM) . More details about this dataset can be found in our project page.

Basic Usage

  1. Download the affinity matrices and meta data of CSM from Google Drive or Baidu Wangpan
  2. Put affnity matrix in "**/data/affinity" and meta data in "**/data/meta". Here "**" means the path that you clone this project to.
  3. Run "matching.py" for visual matching and "propagation.py" for lable propagation. Example:
    python propagation.py --exp in --gpu_id -1 --temporal_link

More Details

  • The downloaded affinity matrices are calculate by the consin simmilarity of the visual features bewteen the instances. More specific, we use face features for cast-tracklet links and body features for tracklet-tracklet links. The face model is a Resnet-101 trained on MS-Celeb-1M. The body model is a Resnet-50 pretrianed on ImageNet and finetune on the training set of CSM. You can also train your own model on CSM.

  • We implement both CPU and GPU version of PPCC, you can choose any one of them by setting the paprameter "gpu_id" (-1 for CPU and others for a specific GPU). The GPU code is based on PyTorch. You are recommand to use GPU version since it is much faster, especially for the "ACROSS" experiment settting.

Citation

@inproceedings{huang2018person,
    title={Person Search in Videos with One Portrait Through Visual and Temporal Links},
    author={Huang, Qingqiu and Liu, Wentao and Lin, Dahua},
    booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
    pages={425--441},
    year={2018}
}