New Models For Real-Time Tracking Using Particle Filtering

This paper presents new methods for efficient 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.