【信息技术】【2010.12】广域运动图像中车辆跟踪与评估的选择技术

本文为美国密苏里大学哥伦比亚分校（作者：KOYELI GANGULI）的硕士论文，共147页。

广域运动图像（WAMI）中的车辆跟踪对于民用和军事监视非常重要。在一个数据集中进行跟踪是一项非常具有挑战性的工作，本研究中的数据集特点是具有非常大的视频格式、非常宽的视场（覆盖数十平方英里）、非常小的地面分辨率（在4000到5000英尺高度拍摄的图像）和较低的帧速率（1-10帧/秒）。目前，分析人员在使用这些数据中花了很多时间进行手动车辆跟踪。目前正在努力实现这一跟踪过程的自动化，从感兴趣的区域选择开始，到最终生成车辆跟踪的轨迹。

本文介绍了开发低帧速率自动辅助车辆跟踪系统的一些技术和方法，并开发了低帧速率跟踪器的性能评估系统。对这个具有挑战性的数据集采取的一种方法是使用数据的地理注册属性，然后从这些图像中提取道路。这使得使用贝叶斯方法的车辆检测算法运行得更快、更高效，此外，汽车跟踪算法可以利用道路的先验知识。Camshift汽车跟踪算法得到了进一步的修改与改进，并在这个数据集中对其进行了定制，以更好地跟踪汽车。本研究开发的性能评估系统，可用来衡量跟踪器在未来研究中的性能提升对比，还可以用于参数调整。该性能评估系统可使用两种方法测试跟踪器的性能，即，使用间隙的方法和轨迹的方法，这两种方法的框架都是基于信息理论测度和非信息理论测度发展起来的。

Tracking of vehicles in wide area motion imagery (WAMI) is very important for civilian and military surveillance. Tracking in a dataset that is characterized by very large format video with an extremely wide field -of-view (covering few to tens of square miles), and with very minimal ground resolution (images taken at about 4000ft to 5000ft above ground) and with low frame rates (1-10 frames/ sec), is a very challenging job. Currently, analysts spend many hours manually tracking vehicles using this data. Efforts are underway to automate this tracking process, starting from region of interest selection to generating the track produced by the vehicle. This research describes some of the techniques and approaches taken towards developing a low frame rate automatic and assisted vehicle tracking system and also develops a performance evaluation system for low frame rate tracker. One approach that is taken on this challenging dataset is extracting roads from these images using the geo-registered property of the data. This makes the car detection algorithms using Bayesian approach run considerably faster and efficiently. Also, car tracking algorithms can use this apriori knowledge of roads. The car tracking algorithm using Camshift has been further modified/improved, customizing it to track cars better in this dataset. A performance evaluation system developed in this research can be used for measuring the performance improvement of the tracker as it advances over the coming years. It can also be used for parameter tuning. This performance evaluation system can be used for testing the tracker performance using two approaches, the approach using gaps and approach using tracklets. Both of these frameworks are developed using information theoretics measures and non-information theoretic measures.

1 引言

2 成像系统、数据特征或挑战以及应用的简单技术

3 WAMI的地理注册与道路网络分析

4 基于贝叶斯网络的汽车检测

5 WAMI中使用CAMSHIFT算法实现车辆跟踪

6 LOFT跟踪器的定量评估

7 结论

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