摘要
视频目标跟踪是计算机视觉研究领域的一个重要组成部分,它融合了计算机图像处理、模式识别、人工智能及自动控制等诸多相关领域的知识,形成了一种能从图像序列中自动检测目标,提取目标位置信息,自动跟踪目标的技术。可用于目标识别、目标分类、行为理解、智能监控等领域,本文针对视频目标跟踪作了研究,主要内容和创新点如下:
1.提出一种视频目标跟踪方法,采用均值漂移和粒子滤波方法跟踪,结合了概率主成分分析目标建模及改进的图切分方法,该方法在精确定位目标的同时能较好获取目标的运动姿态。
2.提出了基于纹理的视频目标检测方法和阴影检测方法,纹理由局部二元图统一模式纹理直方图表征。视频目标检测方法具有较好的通用性,能在一定程度上处理视频运动阴影问题。视频阴影检测方法利用了局部二元图纹理特性,检测出来的阴影较为完整。
3.采用概率主成份分析目标建模方法,较好地解决了目标丢失的问题。利用预测和局部搜索,能在一定程度上解决遮挡问题。验证实验表明了该方法的有效性。
4.设计并完成了一个实验性的视频目标跟踪软件系统,采用模块化实现,由目标检测、阴影检测和目标跟踪等模块组成,进行了实验验证,取得了良好的实验结果。该系统为以后研究工作的实验测试提供了便利。
本文仅研究了在摄像机固定的场景下的目标检测和阴影检测方法,目标跟踪方面主要研究的是单目标的跟踪和简单的遮挡处理,下面的研究工作重点放在动态场景下的结合颜色和纹理等信息的目标检测和阴影检测方法以及复杂场景下的多目标跟踪方法的研究上。
The technology of video based object detection and tracking is one of the hotspots in the field of computer vision,and provide an important data source for visual analysis and understanding.In this dissertation,the research is focused on the three critical problems of visual target tracking - object detection,shadow detection and object tracking.The main contributions of this dissertation are summarized as follows:
1.An object contour tracking algorithm based on particle filter and graph cut has been proposed.We improve the accuracy of the foreground extraction by embedding texture information and discriminative features selection method into the graph cut algorithm.An adaptive tracker based on the integration of particle filter and mean shift has been proposed,which is conciser and effective compared with the existing methods of a kind.The object model utilizing probabilistic principal component analysis gives some guidance to the handling of the occlusion. Experiments on variety real-world video data demonstrate the proposed schem not only locates the target accurately but extracts the objcect contour quite well.
2.A video object detection algorithm and a moving shadow detection algorithm based on texture which is represented by the histogram of the uniform pattern of the local binary pattern(LBP) have been proposed.Video object detection algorithm can extract the moving target quite well and deal with the issue of moving shadow to some extent because of the characteristic of LBR The moving shadow detection algorithm requires only a small number of parameters and experiments on variety real-world video data demonstrate the favorable performance and robustness of the proposed scheme.
3.A tracking algorithm based on the adaptive appearance model has been proposed,in which the adaptive model is utilizing the probabilistic principal component analysis.The object model based on the probabilistic principal component analysis is robuster and more accurate compared with the object model in most existing object tracking method and we can deal with the issue of "object drifting" based on the model,furthermore we can locate the target more accurately and handle the issue of occlusion to some extent by some prediction and local search.We demonstrate the favorable performance and robustness of the proposed scheme by experiments of the integration of this adaptive object model and mean shift method.
4.An enperimental video object tracking system has been designed and verified by experiment results.
Our main work is focused on object detection and shadow detection under a fixed camera scene.When it comes to object tracking,we mainly research on single object tracking.The following work will be focused on the research of object detection and shadow detection under a moving camera scene,and multi-object tracking.
引文
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