Description: This paper presents a complex condition gradient orientation histogram based on subspace tracking methods, through a large number of samples offline training build target subspace projection and gradient orientation histogram in subspace projection as a new target descriptive characteristics. In order to meet the requirements of real-time, using integral histogram method improves the computing speed of the particle characteristics then combined with particle filter in subspace meter grate particles and the similarity between the training sample set, and then estimate the target motion parameters. Experimental results show that this method can change in the light, noise, blur, target attitude and scale changes, and take along some cover in bad condition for accurate tracking, than the traditional tracking method has higher tracking accuracy and robustness to meet the ground reconnaissance missions in a variety of complex conditions accurately track targets of interest requirements.
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复杂环境下的鲁棒目标跟踪方法.pdf
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