New models for real-time tracking using particle filtering

Ka Ki Ng; Edward J. Delp

doi:10.1117/12.807311

19 January 2009 New models for real-time tracking using particle filtering

Ka Ki Ng, Edward J. Delp

Proceedings Volume 7257, Visual Communications and Image Processing 2009; 72570B (2009) https://doi.org/10.1117/12.807311
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

This paper presents new methods for effi9;cient object tracking in video sequences using multiple features and particle filtering. A histogram-based framework is used to describe the features. Histograms are useful because have the property that they allow changes in the object appearance while the histograms remain the same. Particle filtering is used because it is very robust for non-linear and non-Gaussian dynamic state estimation problems and performs well when clutter and occlusions are present. Color histogram based particle filtering is the most common method used for object tracking. However, a single feature tracker loses track easily and can track the wrong object. One popular remedy for this problem is using multiple features. It has been shown that using multiple features for tracking provides more accurate results while increasing the computational complexity. In this paper we address these problems by describing an efficient method for histogram computation. For better tracking performance we also introduce a new observation likelihood model with dynamic parameter setting. Experiments show our proposed method is more accurate and more efficient then the traditional color histogram based particle filtering.

Citation Download Citation

Ka Ki Ng and Edward J. Delp "New models for real-time tracking using particle filtering", Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72570B (19 January 2009); https://doi.org/10.1117/12.807311

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