Particle-filter-based object tracking with color and texture information fusion

Abstract

In this paper, we investigate object tracking in video sequences and propose a particle filter based tracking algorithm with color and texture information fusion, in which the target model is jointly represented by spatial-weighted color histogram and LBP (Local Binary Patterns) texture histogram. The property of local grayscale or color invariance for LBP operator makes it more reliable to measure the spatial structure of local image texture. The system is less sensitive to illumination changes and partial occlusions, and can be able to track objects in diverse conditions. Experimental results demonstrate that the performance of the proposed method is more robust and accurate than the original color based method, especially when tracking objects with similar color appearance to the background and partial occlusions.