G. Franchi, Emanuel Aldea, Séverine Dubuisson, I. Bloch
{"title":"Tracking Hundreds of People in Densely Crowded Scenes With Particle Filtering Supervising Deep Convolutional Neural Networks","authors":"G. Franchi, Emanuel Aldea, Séverine Dubuisson, I. Bloch","doi":"10.1109/ICIP40778.2020.9190953","DOIUrl":null,"url":null,"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.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Image Processing (ICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP40778.2020.9190953","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
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.