Abstract
Object tracking is defined as the problem of estimating object location in image sequences. In general, the problems of object tracking in real time and complex environtment are affected by many uncertainty. In this research we use a sequensial Monte Carlo method, known as particle filter, to build an object tracking algorithm. Particle filter, due to its multiple hypotheses, is known to be a robust method in object tracking task. The performances of particle filter is defined by how the particles distributed. The role of distribution is regulated by the system model being used. In this research, a modified system model is proposed to manage particles distribution to achieve better performance. Object representation also plays important role in object tracking. In this research, we combine color histogram and texture from Local Binary Pattern Histogram Fourier (LBPHF) operator as feature in object tracking. Our experiments show that the proposed system model delivers a more robust tracking task, especially for objects with sudden changes in speed and direction. The proposed joint feature is able to capture object with changing shape and has better accuracy than single feature of color or joint color texture from other LBP variants.
Highlights
Video tracking can be defined as the problem of estimating position of an object over time
In our previous work [10], we found that the performances of object tracking is determined by the spreading of particles, which regulated by the system model being used
[7] proposes to combine color and texture extracted by Local Binary Pattern in mean shift tracking, and [12] uses color and texture generated by DWT for tracking object using Gaussian sum particle filter
Summary
Video tracking can be defined as the problem of estimating position of an object over time. The work reported in [11] combines color, texture, and motion in tracking object by features matching. [7] proposes to combine color and texture extracted by Local Binary Pattern in mean shift tracking, and [12] uses color and texture generated by DWT for tracking object using Gaussian sum particle filter. Ahonen et al in [14] extends LBP operator and uses Discrete Fourier Transform to generate feature that invariant to image rotation.
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