麻省理工学院计算机与人工智能科研项目

Massachusetts Institute of Technology

Computer Science and Artificial Intelligence Research

科研主题

以下领域内的相关课题，具体课题根据学生的基础，导师面试后确定。

machine vision［机器视觉］

machine  learning［机器学习］

sensor fusion for intelligent vehicle and intelligent transportation systems

［智能车辆与智能交通系统的传感器器融合］

big data analysis and systematic development of Universal Village

［环球村背景下大数据分析和系统开发］

an extended version of Smart Cities［智慧城市］

Universal Village［环球村］

MRI image analysis［MRI图像分析］

data analysis for intelligent healthcare［智能医疗数据分析］

科研内容（以往项目参考课题之一）

我们将开发一个三维系统，以可靠的交通监控解决车辆和阴影重叠的问题

The Problem and Goal:

Traffic monitoring systems detect traffic accidents and congestion for immediate assistance and/or traffic flow control. Conventional loop detectors are installed under the pavement and require regular maintenance which is disruptive to traffic. TV-camera based systems are an alternative solution providing non-disruptive monitoring. An advantage of TV-camera-based traffic monitoring systems over loop detectors and acoustic detectors is that the traffic control centers can have a first hand view of the traffic sitution. The problem with conventional TV-camera-based systems is that the systems are confused with overlapping vehicles and shadows, and the systems cannot provide highly accurate measurement results. To avoid overlapping vehicles, overhead structures are used. These structures usually cost more than the machine vision systems. 

Our goal is to develop a three-dimensional system for reliable traffic monitoring solving the problem of overlapping vehicles and shadows.

Approach:
Our traffic monitoring system is a TV-camera based system to provide traffic control centers with a real-time view of the traffic situation. We use our three-dimensional vision system described on page 15 to address the accuracy problems with conventional two-dimensional systems. The cameras are located on the side of the road as shown in Figure 1 and expensive overhung structure is unnecessary. The system uses three-dimensional data to eliminate problems with overlapping vehicles and shadows.

The three-camera system produces an edge depth map containing the distances of the edge of objects in the image. The three-dimensional data is used to distinguish between vehicles and enable traffic monitoring. Figure 2 displays the center image and the edge depth map. The colors indicates the different distances of the object.

Figure 3 is a histogram of the distance vs. number of edges in the depth map. Therefore vehicles are distinguished by the distance from the cameras. Each peak in the histogram correlates to an object in the image.