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Multiple face tracking method in the wild using color histogram features
  • Kwon, Hyuk Jin ;
  • Lee, Seok Hee ;
  • Hosseini, Sepidehsadat ;
  • Moon, Jaewon ;
  • Koo, Hyung Il ;
  • Cho, Nam Ik
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Publication Year
2018-06-18
Journal
2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017, pp.51-55
Mesh Keyword
Association schemesColor histogram featuresConvolutional networksCorrelation filtersData associationMultiple face trackingSelected examplesTracking problem
All Science Classification Codes (ASJC)
Safety, Risk, Reliability and QualityEnergy Engineering and Power TechnologyComputer Networks and CommunicationsComputer Vision and Pattern RecognitionHardware and ArchitectureSignal Processing
Abstract
This paper presents a new framework for the tracking of multiple faces. In order to address this tracking problem in the challenging environments, we first adopt a robust face detector based on Multi-task Cascaded Convolutional Networks (MTCNN) and a very efficient tracker exploiting Kernelized Correlation Filters (KCF). Then, we incorporate the detector and tracker into our framework by proposing a new data association method. In our association scheme, we consider color histogram features as well as geometric overlaps, so that it works robustly in the presence of occlusions and crossovers. We conducted experiments on the selected examples of 300VW database and a challenging test video sequence (TMBS). Experimental results have shown that the proposed method works robustly for challenging scenarios in real time (49 fps).
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36299
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85050157156&origin=inward
DOI
https://doi.org/10.1109/isspit.2017.8388318
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8379924
Type
Conference
Funding
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No.2017-0-00385, Development of User-Context Responsive & Interactive Digital Signage Platform)
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KOO, HYUNG IL구형일
Department of Electrical and Computer Engineering
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