导　　师： 程咏梅

学科专业： H1101

授予学位： 硕士

作　　者： ;

机构地区： 西北工业大学

摘　　要： 运动目标轨迹分类与识别是运动分析中的基本问题，它的功能是解释所监视场景中发生的事件，对所监视场景中运动目标轨迹的行为模式进行分析与识别，智能地做出自动分类；由当前目标所处的状态对将要发生的事件进行预测，根据要求对异常事件进行报警，从而达到安全监控的目的。本文对运动目标轨迹分类与识别作了较深入的研究，主要工作如下： 1.提出了一种新的真实场景中运动目标轨迹有效性判断的方法。在目标跟踪过程中由于刮风等的影响而使一些轨迹受噪声严重干扰，本文基于轨迹长度、轨迹上点坐标值的方差以及运动目标相邻两帧运动方向的角度，对真实场景中跟踪到的轨迹预处理，进行有效性判断从而获得有效的轨迹，为进一步轨迹分类提供样本。 2.应用k均值自动聚类算法，提出了一种新的基于轨迹空间相似距离的轨迹分类算法，对以上获得的有效轨迹进行分类。结果表明该算法分类效果较好，最后获得两个真实场景中6类轨迹共305个轨迹样本，为进一步的轨迹识别奠定基础。 3.深入研究了隐马尔可夫模型的三大经典算法，指出了隐马尔可夫模型算法在应用中的一些问题，及针对这些问题所提出的改进方法。 4.引入改进的隐马尔可夫模型算法进行轨迹识别。针对轨迹的复杂程度对各个轨迹模式类建立相应的隐马尔可夫模型，利用训练样本训练模型得到可靠的模型参数，计算测试样本对于各个模型的最大似然概率，选取最大概率值对应的轨迹模式类作为轨迹识别的结果，对两种场景中聚类后的轨迹进行训练与识别，实验结果表明：识别率分别达到87.76﹪和94.19﹪。 Trajectory classification and recognition of the moving objects are the basic problem of the movement analysis. Its functions are as follows: interpreting what has happened in surveillance scenes, analyzing and recognizing the trajectory activity patterns of the objects in real scenes, classifying them automatically and intelligently. And what will happen is predicted according to the current states of the object, alarms are sent for the abnormal activity in good time. In the end the safety surveillance can be realized. In this thesis, the trajectory classification and recognition are studied in detail. The main contributions are as follows:1. Due to some trajectories are interfered badly by the noises such as wind etc. during the tracking of objects, a novel method that can judge the trajectories's validity is presented. Through analyzing the length, variance of the coordinates and orientation code of the trajectories between two neighbour frames, preprocess them and get the valid ones as samples for further study.2. Using K Means which can automatically cluster trajectories, a new algorithm based on trajectory space similarity distance is presented, and it is applied to classify trajectory. The results are satisfied. Finally, 305 trajectory samples of 6 classes are obtained, and it lays a good foundation for further trajectory recognition.3. A more detailed studying of 3 classic algorithms on HMM is made, some questions in application are pointed, and the corresponding improvement methods are given for them.4. Using HMM to trajectory recognition is introduced. Firstly, aiming at the complex degree of the trajectories, the model are built for every trajectory pattern, and the training samples are used to get the credible parameters of the model, finally, the maximum likelihood probability of test samples are computed to all of the trained model, the maximum value is saved and the corresponding model is the recognition result. Then train and recognize the samples clustered, and recognition rate reach 87.76 /% and 94. 19/% respectively.

关 键 词： 轨迹分类 轨迹识别 有效性判断 自动聚类 隐马尔可夫模型 运动目标 模式识别

分 类 号： [TP13 O235 TP391.4]

领　　域： [自动化与计算机技术] [自动化与计算机技术] [理学] [理学] [自动化与计算机技术] [自动化与计算机技术]