【作者】 杨超；

【导师】 袁淮；

【作者基本信息】 东北大学 ， 计算机软件与理论， 2009， 硕士

【摘要】 运动车辆的有效检测和准确跟踪是现代智能交通系统研究的核心部分。基于视觉的车辆识别由于其良好的适应性和性价比,受到了广泛的关注,是近年来图像处理和人工智能等应用领域的研究热点。该识别算法的主要手段是通过外观特征识别候选目标。然而由于现实环境中复杂的噪声的存在,特定特征的存在、量化和解释往往是模糊的,因此,当前的算法在面临同时提高准确度和敏感度的两难要求时遇到了概念和计算上的双重困难。本文给出了车辆识别问题的数学模型,并提出了一种基于概率模型的动态特征选择方法。通过构建特征有向图的方式,充分的考虑到了真实世界中不同特征对识别结果作用的不平衡性,以及特征之间的相互依赖关系等诸多因素,使得整个识别过程更符合车辆识别是多特征共同匹配、综合作用这一客观规律,是对人类辨别事物过程的一个更近似和更有效的模拟。最后通过与原有后方车辆识别算法的对比实验,从实验结果的ROC曲线可以看出,不仅在多种闽值下提高了识别率,而且准确度也由原来的0.9286提高到了0.9513,进一步证实了该方法是一种可同时提高识别准确度和敏感度,并且有助于解决特征竞争等问题的有效方法。更多还原

【Abstract】 Effective and accurate detection and tracking of moving vehicles in video sequences is the key to modern intelligent traffic monitoring systems. Vision based vehicle recognition has received extensive attentions because of its fine applicability and high cost-performance ratio. Thus, vision based vehicle recognition has become a popular research topic in the field of image processing and artificial intelligence. The recognition algorithm mainly implements the synthesis which uses both appearance based and knowledge based features to identify its candidates. However, due to the unpredicted complex noises in real world environments, existences, quantifications and explanations for certain features are often ambiguous, which makes current algorithm hard to fulfill the dilemmatic high sensitivity/accuracy restriction, and an improvement for a certain feature (or data sets) often leads to degeneration for others. This dissertation provides a mathematical model for vehicle recognition, and proposes a probability model based feature selection method which enables the dynamic feature selection and multigrain feature evaluations. The method of feature digraph construction takes very sufficient thought of the fact that, in the real world, the imbalance that different features result in different outcomes of recognitions; and the inter-dependencies and the inter-relationships among different features, etc. By doing so it makes the entire recognition process converge to the objective laws that vehicle recognitions is of multi-feature matching and collaboration. It is the more accurate and more effective imitation of the process of human-recognitions. Eventually, by comparing with the former rear vehicle recognition algorithms in experiments, ROC curves show that not only the recognition rate was improved in a variety of thresholds, but also the accuracy was raised from 0.9286 to 0.9513, it further proves that the method (mentioned above) is an effective solution which can increase the accuracy and sensitivity of recognitions, and it is very helpful in solving feature-racing alike problems.更多还原