基于量子演化理论的行人检测改进算法

对视频中移动摄像头下的行人检测问题进行了研究,在AdaBoost行人分类算法、支持向量机(SVM)理论和多目标优化原理的基础之上,并结合三者的特点,提出了一种基于量子演化算法的行人检测优化算法。首先,使用传统的AdaBoost算法对行人进行粗粒度的分类,然后使用支持向量机(SVM)设计精度更高的行人检测器。针对SVM的分类器参数多、关系复杂,而且无好的调节准则,根据核函数的构建条件,将实值量子演化算法引入到SVM参数的寻优问题中,对于分类性能采用多目标优化的方法,取得了较好的效果;同时从理论上分析了算法的复杂度。经过实例测试,算法与经典多目标优化算法NSGA-II的相比,改进效果明显。最后的实验说明了算法检测的准确性。

An optimization pedestrian detection algorithm based on quantum evolution

This paper proposes proposed an optimization pedestrian detection algorithm based on quantum evolution. This approach bases on AdaBoost pedestrian detection algorithm, supporting vector machine (SVM) and multi-objective optimization theory as the basis, and the core of the approach is quantum evolution which bases on real encoding. Firstly, it utilizes the AdaBoost to classify pedestrian with coarse granularity, and then employ SVM to design more accurate pedestrian detector. Taking multi-parameter with complex relationships and no reasonable regulation criteria into account, this paper considers the construction condition of kernel function, introduce real quantum evolution algorithm to the domain of SVM parameter optimization problems, and adopts multi-objective optimization concept to enhance the classification performance, which achieves good results. Meanwhile the complexity of the algorithm has been analyzed in theory to ensure the real-time characteristic.