Traffic Information Acquisition based on Robust Tracking Techniques Using Images and Sound

基于从交通视频中可以提取出各种对智能交通系统（ITS）实现有用的信息的思想，大量的研究工作致力于发展基于交通监控序列的车辆跟踪技术。然而，由于鲁棒性的问题，跟踪技术仍然很难投入实际使用。本研究的目的是通过提高车辆跟踪技术的鲁棒性来扩大车辆跟踪技术的应用范围，在科研资助项目的研究中，首先建立了一种低层跟踪过程与高层跟踪过程相结合的跟踪方法，使目标对象在高维状态空间中随时间的变化得到跟踪，以适应光照条件和物体运动快速变化的影响。利用这种方法，我们实现了对一个典型的计算机系统的计算， 关于我们 其次，在上述框架下，重点研究并实现了作为底层跟踪功能的识别机制。这种低层跟踪过程既可以单独作为跟踪器工作，也可以与高层跟踪过程联合工作，使整个系统更加可靠。另外，（1）即使在恶劣天气、夜晚或发生遮挡的情况下也保持系统的鲁棒性（由于车辆在图像上的重叠），我们提出了一种基于立体声麦克风采集的道路交通声音识别的车辆通过检测方法，并证明了该方法的有效性，特别是对于夜间图像序列;（2）为了在没有道路基础设施的情况下获得必要的信息，我们研究并开发了一种通过使用车载摄像机来检测和跟踪相邻车辆的方法;（3）为保障安全驾驶，我们开发了交叉口车辆和行人的识别和区分技术，相关研究成果已在多个国际会议和期刊上发表。少

Based on the idea that a variety of information useful for realization of Intelligent Transportation Systems (ITS) can be extracted from traffic movies, a lot of research efforts have been deveted to develop vehicle tracking techniques using traffic surveillance sequences. However, because of issues in robustness, the tracking techniques are still difficult to be put to practical use. The purpose of this research is to expand the application scope of the vehicle tracking techniques by greatly enhancing the robustness of them.In the period of the research supported by Grant-In-Aid for Scientific Research, first we have established a tracking method that integrates low-level tracking process with the high-level tracking process, which enables transformation of target objects to be tracked over time in high-dimensional state space, in Bayes framework to adapt to the influence from rapid changes of lighting conditions and object's motions. By using this method, we have achieved the compati … More bleness between robustness and real-time execution of tracing.Secondly, within above-mentioned framework, recognition mechanism that functions as low-level tracking has been mainly investigated and implemented. This low-level tracking process can not only work individually as a tracker but can also work jointly with high-level tracking process to make the whole system more reliable. Additionally, (1) to keep the robustness of the system even in case of bad weather, night or occurrence of occlusion (because of overlap of vehicle on images), we have developed a method to detect the passages of vehicles based on recognizing road traffic sound taken by a stereo microphones and proved its effectiveness especially for night-image-sequences ; (2) to obtain necessary information in the situation where road-side-infrastructures are not available, we have investigated and developed a method to detect and track neighboring vehicles by using in-vehicle cameras ; (3) to support safe driving, we have developed the techniques to identify and distinguish vehicles and pedestrian at intersections.The results related to this research project have been published in several proceedings of international canferences and journals. Less