@inproceedings{2a1c52dc01d14a96ba84a3c3f56c5d68,
title = "Tracking Hundreds of People in Densely Crowded Scenes with Particle Filtering Supervising Deep Convolutional Neural Networks",
abstract = "Tracking an entire high-density crowd composed of more than five hundred individuals is a difficult task that has not yet been accomplished. In this article, we propose to track pedestrians using a model composed of a Particle Filter (PF) and three Deep Convolutional Neural Networks (DCNN). The first network is a detector that learns to localize the persons. The second one is a pretrained network that estimates the optical flow, and the last one corrects the flow. Our contribution resides in the way we train this last network by PF supervision, and in Markov Random Field linking the different tracks.",
keywords = "Computer Vision, Crowd tracking, Deep learning, Self supervised learning",
author = "Gianni Franchi and Emanuel Aldea and Severine Dubuisson and Isabelle Bloch",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Image Processing, ICIP 2020 ; Conference date: 25-09-2020 Through 28-09-2020",
year = "2020",
month = oct,
day = "1",
doi = "10.1109/ICIP40778.2020.9190953",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "2071--2075",
booktitle = "2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings",
}