Development of the methods for tracking and behavior understanding of vehicles and pedestrians in urban traffic scenes

城市交通场景中车辆和行人的跟踪和行为理解方法的开发

基本信息

批准号：
14580407
负责人：
KAMIJO Shunsuke
金额：
$ 2.05万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2003
项目状态：
已结题

项目摘要

Last fiscal year, we developed an algorithm which is able to track both rigid objects such as vehicles and flexible objects such as pedestrians in images.This fiscal year, improving this tracking algorithm, we enabled to get detail trajectory of these various types of moving objects, and implemented a prototype of Real-time System.On the other hand, we developed a system which detects events based on experiments mainly conducted on Metropolitan Expressway.By applying the Spatio-Temporal trajectory of each object from the algorithm, this system classifies traffic events by using a rule-based reasoning method.First Coordinate-class operator extracts Spatio-Temporal trajectory of each vehicle from tracking results.Secondly, Behavior-class operator extracts behavior of each vehicle such as "Stall" and "Stop" from the Spatio-Temporal trajectories.Lastly, using behaviors information of vehicles on the image, Event-class operator detects traffic events such as accidents, congestion, etc.As above, our event detection, system utilizes a semantic hierarchy consisting of three kinds of operators (Coordinate-class, Behavior-class and Event-class) and has high affinity with the human cognitive process.So, it can be said that development of algorithm which tracks vehicles and pedestrians simultaneously achieved some positive success. When it came to the event detection, fundamental algorithm was developed to recognize behaviors of vehicles in particular. We consider that this algorithm can also be applied to images including vehicles and pedestrians, and will continue the verification of the system extensibility.

上一财年，我们开发了一种算法，能够跟踪图像中的车辆等刚性物体和行人等柔性物体。本财年，改进了这种跟踪算法，我们能够获得这些不同类型移动物体的详细轨迹，并实现了实时系统的原型。另一方面，我们开发了一个基于主要在首都高速公路上进行的实验的事件检测系统。通过应用算法中每个对象的时空轨迹，该系统对交通进行分类通过使用基于规则的事件首先，坐标类算子从跟踪结果中提取每辆车的时空轨迹。其次，行为类算子从时空轨迹中提取每辆车的行为，例如“失速”和“停止”。最后，使用图像上车辆的行为信息，事件类算子检测交通事件，例如事故、拥堵等。如上所述，我们的事件检测系统利用由三种算子组成的语义层次结构（坐标类、行为类和事件类）与人类认知过程具有很高的亲和力。因此，可以说同时跟踪车辆和行人的算法的开发取得了一些积极的成功。在事件检测方面，开发了基本算法来识别车辆的特定行为。我们认为该算法也可以应用于包括车辆和行人的图像，并将继续验证系统的可扩展性。