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Particle filter based on context tracking algorithm for real-world hazy scenes

Recently, a large scale air pollution problems, such as haze and fog bring challenges for target tracking in computer vision. However, most existing tracking algorithms are far from satisfactory in terms of adaptability to complex scenes and restoring the image's quality for hazy scenes. To address this issue, a particle filter based on context tracking algorithm is proposed for real-world hazy scenes. The state estimation of the particle filter and spatio-temporal context correlation are incorporated with popular dahazing algorithms. Compared with the state-of-the-art tracking algorithms, qualitative and quantitative experiment results have demonstrated that the proposed algorithm effectively achieves accurate and robust target tracking, as well as enhances the visual qualities of the restored images simultaneously.