{"title":"A motion-enhanced hybrid Probability Hypothesis Density filter for real-time multi-human tracking in video surveillance scenarios","authors":"Volker Eiselein, T. Senst, I. Keller, T. Sikora","doi":"10.1109/PETS.2013.6523789","DOIUrl":null,"url":null,"abstract":"The Probability Hypothesis Density (PHD) filter is a multi-object Bayes filter which has been recently becoming popular in the tracking community especially for its linear complexity and its ability to filter out a high amount of clutter. However, its application to Computer Vision scenarios can be difficult as it requires high detection probabilities. Many human detectors suffer from a significant miss-match rate which causes problems for the PHD filter. This article presents an implementation of a Gaussian Mixture PHD (GM-PHD) filter which is enhanced by Optical Flow information in order to account for missed detections. We give a detailed mathematical discussion for the parameters of the proposed system and justify our results by extensive tests showing the performance in several contexts and on different datasets.","PeriodicalId":385403,"journal":{"name":"2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PETS.2013.6523789","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The Probability Hypothesis Density (PHD) filter is a multi-object Bayes filter which has been recently becoming popular in the tracking community especially for its linear complexity and its ability to filter out a high amount of clutter. However, its application to Computer Vision scenarios can be difficult as it requires high detection probabilities. Many human detectors suffer from a significant miss-match rate which causes problems for the PHD filter. This article presents an implementation of a Gaussian Mixture PHD (GM-PHD) filter which is enhanced by Optical Flow information in order to account for missed detections. We give a detailed mathematical discussion for the parameters of the proposed system and justify our results by extensive tests showing the performance in several contexts and on different datasets.
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