【作者】 王璐；

【导师】 羊恺；

【作者基本信息】 电子科技大学 ， 导航、制导与控制， 2017， 硕士

【摘要】 随着生产生活水平的提高,汽车保有量大幅增加,汽车交通事故也愈发频繁。车载红外夜视系统是先进驾驶辅助系统的一部分,具有日夜皆可、不受雨雪或浓雾等恶劣天气影响的优点。并且随着工艺水平的提高,其硬件制作难度和成本不断降低,因而逐渐成为车辆辅助驾驶系统的主角。然而目前大多数的车载红外夜视系统只是简单地将车辆前方的图像采集然后显示出来,并未对图像进行进一步的处理,因此对司机的提醒效果十分有限。本文正是基于这一现实,研究对车载夜视红外图像进行行人检测和跟踪,主要研究工作如下:(1)介绍红外夜视技术的分类和原理,对热成像技术的指标和特点进行了概括。此外,从灰度直方图、噪声和分辨率三个方面对红外图像的特性进行了分析。(2)根据红外图像特性分析的结果确定了车载红外图像预处理的方法。首先,采用中值滤波滤除噪声、直方图均衡增强红外图像对比度。然后在Otsu算法的基础上进行改进实现图像分割,接着又进行数学形态学处理弥合空洞,最后对连通域进行区域标记和过滤。经过上述处理,最终实现行人区域的粗提取即感兴趣区域提取,这一步骤能够有效提升行人检测算法的实时性。(3)从理论框架和仿真实现两个方面对行人识别的统计分类方法进行了阐述和论证。针对支持向量机性能依赖核参数大小的特性,融合支持向量机和Adaboost算法设计分类器,对感兴趣区域归一化到规定大小然后提取出的HOG特征进行判别,从而确定该感兴趣区域是否为行人。实验结果表明,本文所设计的分类器能够自适应地调整支持向量机的参数,既解决了单一支持向量机使用固定核参数值的问题,同时也处理了精确度与复杂度之间的平衡问题,与单一的支持向量机相比具有更好的分类效果。(4)在行人跟踪阶段,研究了 Mean Shift跟踪算法和Kalman滤波跟踪算法的原理和方法,在对这两种跟踪方法的优缺点进行分析后,研究并实现了一种Kalman滤波与自适应窗口的Mean Shift融合的跟踪方法,这种跟踪算法针对单个行人、行人发生形变以及简单遮挡的情况具有较好的跟踪效果。更多还原

【Abstract】 With the improvement of production and living standards, the number of car ownership has increased greatly, and more and more traffic accidents have occurred.Vehicular infrared night-vision system is a part of advanced driver assistant system. It can be used both in day and night, rain and snow, fog and other inclement weather. And with the improvement of the process level, the difficulty and cost of vehicular infrared night-vision system’s hardware production is decreasing. Based on the above advantages,vehicular infrared night-vision system has become the lead of the auxiliary driving system. However, most of the current vehicular infrared night-vision system just simply collect the image in front of vehicle, and then display in screen. The image has not been further processed, so the warning effect is limited. Based on this reality, this thesis studies the pedestrian detection and tracking on vehicular infrared night-vision’s infrared image. The main research work is as follows:(1) Illustrated the classification and principle of infrared night-vision technology,summarized the indexes and characteristics of thermal imaging technology. In addition,the characteristics of the infrared image are analyzed from the three aspects of gray histogram, noise and resolution.(2) According to the results of infrared image characteristic analysis, the method of vehicle infrared image preprocessing is established. Firstly, the median filter is used to filter noise and histogram equalization is used to enhance the contrast of infrared image.Then, use the improved Otsu algorithm to segment the image and mathematical morphological processing to deal with the hole. Finally, mark and filter the connected domain. After the above treatment, we got the region of rough pedestrian area that is region of interests. This step can effectively improve the real-time performance of pedestrian detection algorithm.(3) Describe and demonstrate from the theoretical framework to simulation for in statistical classification algorithm of pedestrian recognition. The performance of support vector machines depends on the size of the kernel parameters. According to this feature,we combine the support vector machine and Adaboost algorithm to design the classifier.The classifier is used to judge the HOG features extracted from the region of interests after normalized to specified size to determine whether it is a pedestrian. Experiments indicate that the classifier designed in this thesis can adaptively adjust the parameters of support vector machine, which solves the problem of using one fixed kernel parameter,and also deals with the balance between precision and complexity. Thus, it has a better classification effect than the single support vector machine.(4) In the stage of pedestrian tracking, this thesis analyzed the principle and method of Mean Shift tracking algorithm and Kalman filter tracking algorithm. After analyzed the advantages and disadvantages of these two tracking methods, a tracking method combining Kalman filter with adaptive window of Mean Shift is proposed. The tracking algorithm designed in this thesis has good tracking effect in single pedestrian,pedestrian deformation and simple occlusion scene.更多还原