基于结构支持向量机的目标跟踪算法研究
发布时间：2018-10-17 19:09
【摘要】：随着科技进步和社会发展,计算机视觉跟随人工智能的脚步走入人类视野。目标检测与跟踪课题,作为计算机视觉的关键问题,也是一个经典难题,近年来受到各个相关领域研究学者的关注,并且应对不同场景探索不同的检测与跟踪算法。在目标检测和目标跟踪两个领域中,关键的问题都在于如何有效描述目标、如何让计算机准确识别目标;不同点在于检测看重的是精确度,而跟踪在于实时性。应对这两种需求,本文结合支持向量机的优秀分类特性,研究了以下检测和跟踪系统。对于目标检测系统,在其训练阶段,首先在每个滑动窗口中分别计算HOG特征与LBPHF特征,然后将两者结合构成联合特征。接着利用线性支持向量机(SVM)训练分类器,其中本算法通过自举法(Bootstrap Method)不断更新优化分类器,以此获得最优判别模型。在训练阶段的基础上,将提取所得的联合特征输入上一阶段所获得的分类器中进行判别,最后采用非极大值抑制(NMS)的融合方法对重叠检测窗口进行融合,以此获得最终的检测结果。实验证明改进后的方法满足检出率高、计算复杂度低、抗行人肢体偏转干扰能力强等要求。对于目标跟踪系统,首先利用无模型的跟踪框架,运用改进的HOG-LBPHF对目标进行表观,并且结合目标间的结构信息,以此来训练SVM。其次采用被动主动感知器对分类平面进行优化。最后用最小生成树模型确定下一帧的所在位置。经过实验对比,本算法具有良好的跟踪性能。
[Abstract]:With the progress of science and technology and social development, computer vision follows the pace of artificial intelligence into human vision. Target detection and tracking, as a key problem of computer vision, is also a classical problem. In recent years, it has attracted the attention of researchers in various related fields, and different detection and tracking algorithms should be explored in different scenes. In the two fields of target detection and target tracking, the key problems are how to describe the target effectively and how to accurately identify the target by computer. In response to these two requirements, this paper studies the following detection and tracking systems combined with the excellent classification characteristics of support vector machines. In the training phase of the target detection system, the HOG feature and the LBPHF feature are calculated in each sliding window, and then the two features are combined to form a joint feature. Then the classifier is trained by linear support vector machine (SVM), where the optimal discriminant model is obtained by updating the optimal classifier by bootstrap (Bootstrap Method). On the basis of the training stage, the extracted joint features are input into the classifier obtained in the previous stage to discriminate. Finally, the overlapping detection window is fused using the fusion method of non-maximum suppression (NMS). Finally, the final test results are obtained. The experimental results show that the improved method meets the requirements of high detection rate, low computational complexity and strong anti-pedestrian limb deflection interference. For the target tracking system, we first use the model-free tracking framework, use the improved HOG-LBPHF to visualize the target, and combine the structure information between the targets to train the SVM.. Secondly, passive active perceptron is used to optimize the classification plane. Finally, the location of the next frame is determined by the minimum spanning tree model. The experimental results show that the algorithm has good tracking performance.
【学位授予单位】：哈尔滨理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41;TP18
本文编号：2277675
[Abstract]:With the progress of science and technology and social development, computer vision follows the pace of artificial intelligence into human vision. Target detection and tracking, as a key problem of computer vision, is also a classical problem. In recent years, it has attracted the attention of researchers in various related fields, and different detection and tracking algorithms should be explored in different scenes. In the two fields of target detection and target tracking, the key problems are how to describe the target effectively and how to accurately identify the target by computer. In response to these two requirements, this paper studies the following detection and tracking systems combined with the excellent classification characteristics of support vector machines. In the training phase of the target detection system, the HOG feature and the LBPHF feature are calculated in each sliding window, and then the two features are combined to form a joint feature. Then the classifier is trained by linear support vector machine (SVM), where the optimal discriminant model is obtained by updating the optimal classifier by bootstrap (Bootstrap Method). On the basis of the training stage, the extracted joint features are input into the classifier obtained in the previous stage to discriminate. Finally, the overlapping detection window is fused using the fusion method of non-maximum suppression (NMS). Finally, the final test results are obtained. The experimental results show that the improved method meets the requirements of high detection rate, low computational complexity and strong anti-pedestrian limb deflection interference. For the target tracking system, we first use the model-free tracking framework, use the improved HOG-LBPHF to visualize the target, and combine the structure information between the targets to train the SVM.. Secondly, passive active perceptron is used to optimize the classification plane. Finally, the location of the next frame is determined by the minimum spanning tree model. The experimental results show that the algorithm has good tracking performance.
【学位授予单位】：哈尔滨理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41;TP18
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