An on-line visual human tracking algorithm using SURF-based dynamic object model

Meenakshi Gupta, Sourav Garg, Swagat Kumar, Laxmidhar Behera

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Citations (Scopus)

Abstract

The interest point based tracking methods suffer from the limitation of unavailability of sufficient number of matching key points for the target in all frames of a running video. In this paper, a dynamic model is proposed for describing the object model which is used for tracking a human in a non-stationary video. This dynamic model takes into account the change in the pose as well as the motion of the human. A simple autoregression based predictor is used for dealing with the case of full occlusion. Simulation results are provided to show the efficacy of the algorithm. © 2013 IEEE.
Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages3875-3879
Number of pages5
DOIs
Publication statusPublished - 13 Feb 2014
Event 2013 IEEE International Conference on Image Processing - Melbourne, Australia
Duration: 15 Sep 201319 Sep 2013

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference 2013 IEEE International Conference on Image Processing
CountryAustralia
CityMelbourne
Period15/09/1319/09/13

Keywords

  • Auto-regression prediction
  • Human Tracking
  • SURF

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    Gupta, M., Garg, S., Kumar, S., & Behera, L. (2014). An on-line visual human tracking algorithm using SURF-based dynamic object model. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings (pp. 3875-3879). (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings). https://doi.org/10.1109/ICIP.2013.6738798