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Robust Real-time Car Tracking Unifying Low-level and High-level Tracking In a Stochastic Framework

鲁棒的实时汽车跟踪在随机框架中统一低级和高级跟踪

基本信息

批准号：
13680442
负责人：
KATO Jien
金额：
$ 2.62万
依托单位：
NAGOYA UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2002
项目状态：
已结题

项目摘要

Automatic traffic surveillance systems based on visual tracking techniques has been desired for many years, since it realizes many functions of ITS (Intelligent Transportation System). These functions includes estimating the number of the cars, speed, types of cars, and detecting some exceptional events (accidents, traffic jams). This technique may lead to the new services relating to the dynamic traffic information, course instruction or automatic drive operation. However, the robustness of the visual tracking is not sufficient for the practical systems.We have already developed an HMM-based background model for background- object-shadow separation and verified its validity on the low-level tracking system. In this research project, we intend to integrate the low-level tracker using the HMM-based background model with the high-level tracker which are able to track the deformation of the target object, into a probabilistic framework (Bayes' framework). This framework realiaes the practical tracking with the robustness to the change of light condition and to the rapid motion of the target object. We have designed and proposed the detailed framework of this mechanism. And we also discuss some problems such as the initialization of the tracking process and the possibility of the multi-object tracking, which are essential at the practical system design phase.This research project has been done for two years (April 2001 to March 2003). The result has been made public through the magazines of the academic societies, and the proceedings of the international conferences and other academic workshops.

基于视觉跟踪技术的自动交通监控系统实现了智能交通系统（ITS）的许多功能，因此一直备受关注。这些功能包括估计汽车的数量、速度、汽车的类型以及检测一些异常事件（事故、交通堵塞）。该技术可能导致与动态交通信息、路线指示或自动驾驶操作相关的新服务。然而，视觉跟踪的鲁棒性在实际系统中并不足够，我们已经提出了一种基于HMM的背景模型来分离背景-目标-阴影，并在底层跟踪系统中验证了其有效性。在这个研究项目中，我们打算将使用基于HMM的背景模型的低级跟踪器与能够跟踪目标对象的变形的高级跟踪器集成到概率框架（贝叶斯框架）中。该框架实现了对光照条件变化和目标物体快速运动的鲁棒性跟踪。我们已经设计并提出了这一机制的详细框架。同时，本文还讨论了在实际系统设计阶段需要解决的一些问题，如跟踪过程的初始化和多目标跟踪的可能性等。研究结果已通过学术团体杂志、国际会议记录和其他学术研讨会公布。