Tracking Multi-Objects in Web Camera Video Using Particle Filtering

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 235)


This paper presents new methods for efficient object tracking in web camera video sequences using multiple features and particle filtering. Particle filtering is particularly useful in dealing with nonlinear state space models and non-Gaussian probability density functions. We develop a multi-objects tracing system which considers color information, distance transform based shape information and also nonlinearity. We examine the difficulties of video based tracking and step by step we analyze these issues. In our first approach, we develop the color based particle filter tracker that relies on the deterministic search of window, whose color content matches a reference histogram model. A simple HSV histogram-based color model is used to develop this observation system. Secondly, we describe a new approach for moving multi-object tracking with particle filter by shape information. The shape similarity between a template and estimated regions in the video scene is measured by their normalized cross-correlation of distance transformed images. Our observation system of particle filter is based on shape from distance transformed edge features. Template is created instantly by selecting any object from the video scene by a rectangle. Finally, in this paper we illustrate how our system is improved by using both these two cues with nonlinearity.


Particle filter Multi-target tracking Condensation Video image 


  1. 1.
    Lee YW (2011) Automation of an interactive interview system by hand gesture recognition using particle filter. Int J Marit Inf Commun Sci 9(6):633–636Google Scholar
  2. 2.
    Huang DS, Ip HHS, Law KCK, Chi Z (2005) Zeroing polynomials using modified constrained neural network approach. IEEE Trans Neural Networks 16(3):721–732CrossRefGoogle Scholar
  3. 3.
    Kang CG, Kim DH (2011) Designing of dynamic sensor networks based on meter-range swarming flight type air nodes. Int J Marit Inf Commun Sci 9(6):625–628MathSciNetGoogle Scholar
  4. 4.
    Zhao ZQ, Huang DS, Jia W (2007) Palmprint recognition with 2DPCA + PCA based on modular neural networks. Neurocomputing 71(1–3):448–454CrossRefGoogle Scholar
  5. 5.
    Liu J, Wu J (2001) Multi-agent robotic systems. CRC Press, Boca RatonCrossRefGoogle Scholar
  6. 6.
    Yeo TK, Hong S, Jeon BH (2010) Latest tendency of underwater multi-robots. J Inst Control Rob Syst 16(1):23–34Google Scholar
  7. 7.
    Lee YW (2010) Implementation of code generator of particle filter. Int J Marit Inf Commun Sci 8(5):493–497Google Scholar
  8. 8.
    Lee YW (2008) Development of tracking filter for the location awareness of moving objects in ubiquitous computing. Int J Marit Inf Commun Sci 6(1):86–90Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Information and Communication EngineeringHonam UniversityGwangjuSouth Korea

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