Article

Particle Filter Based Object Tracking with Sift and Color Feature

Authors:

Saeid Fazli,

Hamed Moradi Pour,

Hamed BouzariAuthors Info & Claims

ICMV '09: Proceedings of the 2009 Second International Conference on Machine Vision

Pages 89 - 93

https://doi.org/10.1109/ICMV.2009.47

Published: 28 December 2009 Publication History

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Abstract

Visual object tracking is an important topic in multimedia technologies. This paper presents robust implementation of an object tracker using a vision system that takes into consideration partial occlusions, rotation and scale for a variety of different objects. A scale invariant feature transform (SIFT) based color particle filter algorithm is proposed for object tracking in real scenarios. The Scale Invariant Feature Transform (SIFT) has become a popular feature extractor for vision based applications. It has been successfully applied for metric localization and mapping. Then the object is tracked by a color based particle filter. The color particle filter has proven to be an efficient, simple and robust tracking algorithm. Experimental results of applying this technique show improvement in tracking and robustness in recovering from partial occlusions, rotation and scale.

Cited By

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Yuan DLu XLi DLiang YZhang X(2019)Particle filter re-detection for visual tracking via correlation filtersMultimedia Tools and Applications10.1007/s11042-018-6800-078:11(14277-14301)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s11042-018-6800-0
Devyatkov VAlfimtsev ATaranyan A(2018)Multicamera Human Re-Identification based on Covariance DescriptorPattern Recognition and Image Analysis10.1134/S105466181802002528:2(232-242)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1134/S1054661818020025
Zhao ZFeng PWang TLiu FYuan CGuo JZhao ZCui Z(2017)Dual-scale structural local sparse appearance model for robust object trackingNeurocomputing10.1016/j.neucom.2016.09.031237:C(101-113)Online publication date: 10-May-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.09.031
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Particle Filter Based Object Tracking with Sift and Color Feature
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Machine learning
    1. Machine learning algorithms

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ICMV '09: Proceedings of the 2009 Second International Conference on Machine Vision

December 2009

325 pages

ISBN:9780769539447

Publisher

IEEE Computer Society

United States

Publication History

Published: 28 December 2009

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Yuan DLu XLi DLiang YZhang X(2019)Particle filter re-detection for visual tracking via correlation filtersMultimedia Tools and Applications10.1007/s11042-018-6800-078:11(14277-14301)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s11042-018-6800-0
Devyatkov VAlfimtsev ATaranyan A(2018)Multicamera Human Re-Identification based on Covariance DescriptorPattern Recognition and Image Analysis10.1134/S105466181802002528:2(232-242)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1134/S1054661818020025
Zhao ZFeng PWang TLiu FYuan CGuo JZhao ZCui Z(2017)Dual-scale structural local sparse appearance model for robust object trackingNeurocomputing10.1016/j.neucom.2016.09.031237:C(101-113)Online publication date: 10-May-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.09.031
Jeyabharathi DDejey D(2016)Vehicle Tracking and Speed Measurement system (VTSM) based on novel feature descriptorJournal of Visual Communication and Image Representation10.1016/j.jvcir.2016.08.01140:PB(816-830)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.jvcir.2016.08.011

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An adaptive particle filter tracking method based on homography and common FOV

Single object tracking using particle filter framework and saliency-based weighted color histogram

Object Tracking with Appearance-based Kalman Particle Filter in Presence of Occlusions

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