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首页> 外文期刊>Research Journal of Applied Sciences: RJAS >An Efficient Object Detection and Tracking System Based on Immune Genetic Algorithm
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An Efficient Object Detection and Tracking System Based on Immune Genetic Algorithm

机译：基于免疫遗传算法的高效目标检测与跟踪系统

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摘要

Particle filter algorithm is widely used for target tracking using video sequences which is of greatimportance for intelligent surveillance applications. However, there is still much room for improvement, e.g., theso-called "sample impoverishment". A novel algorithm, the Immune Genetic Algorithm (IGA) is proposed basedon the theory of immunity in biology which mainly constructs an immune operator accomplished by two steps:a vaccination and an immune selection. In our proposed methodology a sample video is given as input for thepurpose of object tracking. The steps to be carried out in our proposed method are given: motion segmentation,noise removal and object tracking. Motion segmentation involves the generation of background model for thepurpose of segmentation. Noise removal involves the particle filters. The main process that takes place in theparticle filter is re-sampling. Then the object was tracked by using immune Genetic algorithm. Immune Geneticalgorithm involves vaccination and the immune selection process including the process of ordinary Geneticalgorithm. This efficiently involves the multiple object detection.
机译：粒子滤波算法被广泛用于视频序列的目标跟踪，这对于智能监控应用非常重要。但是，还有很多改进的空间，例如所谓的“样品贫困”。基于生物学的免疫学原理，提出了一种新的算法，即免疫遗传算法（IGA），该算法主要构建了通过免疫接种和免疫选择两个步骤完成的免疫算子。在我们提出的方法中，出于目标跟踪的目的，给出了示例视频作为输入。给出了我们提出的方法中要执行的步骤：运动分割，噪声去除和目标跟踪。运动分割涉及用于分割目的的背景模型的生成。噪声消除涉及颗粒过滤器。粒子过滤器中发生的主要过程是重新采样。然后使用免疫遗传算法对目标进行跟踪。免疫遗传算法涉及疫苗接种和免疫选择过程，包括普通遗传算法。这有效地涉及多目标检测。

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