Browsing by Author "Tang, Hua"

Now showing 1 - 5 of 5
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    Development of a Multiple-Camera Tracking System for Accurate Traffic Performance Measurements at Intersections
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-02) Tang, Hua
    Automatic traffic data collection can significantly save labor work and cost compared to manual data collection. However, automatic traffic data collection has been one of the challenges in Intelligent Transportation Systems (ITS). To be practically useful, an automatic traffic data collection system must derive traffic data with reasonable accuracy compared to a manual approach. This project presents the development of a multiple-camera tracking system for accurate traffic performance measurements at intersections. The tracking system sets up multiple cameras to record videos for an intersection. Compared to the traditional single-camera based tracking system, the multiple-camera one can take advantage of significantly overlapped views of the same traffic scene provided by the multiple cameras such that the notorious vehicle occlusion problem is alleviated. Also, multiple cameras provide more evidence of the same vehicle, which allows more robust tracking of the vehicle. The developed system has mainly three processing modules. First, the camera is calibrated for the traffic scene of interest and a calibration algorithm is developed for multiple cameras at an intersection. Second, the system tracks vehicles from the multiple videos by using powerful imaging processing techniques and tracking algorithms. Finally, the resulting vehicle trajectories from vehicle tracking are analyzed to extract the interested traffic data, such as vehicle volume, travel time, rejected gaps and accepted gaps. Practical tests of the developed system focus on vehicle counts and reasonable accuracy is achieved.
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    Development of a New Tracking System Based on CMOS Vision Processor Hardware, Phase II Prototype Demonstration
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-05) Tang, Hua; Peng, Li
    Intelligent transportation systems depend on being able to track vehicle operations and collect accurate traffic data. This project targets a hardware-based video detection system for real-time vehicle detection. To allow real-time detection, customized hardware implementation of the system is targeted instead on the traditional computer-based implementation of the system. The system includes four main processing steps. First, a camera is used to capture images. Second, the captured images are segmented using the Mixture-of-Gaussian algorithm. Without sacrificing the segmentation accuracy, researchers modified the Mixture-of-Gaussian algorithm to allow more efficient and economical hardware implementation in terms of design overhead and hardware resources. Third, the segmentation regions are extracted and validated as the objects of interests. In the last step, the validation result will be wirelessly transmitted to a variable message sign, which displays necessary traffic information. Since the system design includes integration of diverse devices, the video design kit from Xilinx is used. Such a hardware-based vehicle detection system has been experimented tested with practical videos of traffic scene.
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    Development of a New Tracking System based on CMOS Vision Processor Hardware: Phase I
    (University of Minnesota Center for Transportation Studies, 2009-02) Tang, Hua
    It is well known that vehicle tracking processes are very computationally intensive. Traditionally, vehicle tracking algorithms have been implemented using software approaches. The software approaches have a large computational delay, which causes low frame rate vehicle tracking. However, real-time vehicle tracking is highly desirable to improve not only tracking accuracy but also response time, in some ITS (Intelligent Transportation System) applications such as security monitoring and hazard warning. For this purpose, this project proposes a hardware based vehicle tracking system for real-time high frame rate tracking. The proposed tracking algorithm is based on motion estimation by full-search block matching algorithm. Motion estimation is implemented in a hardware processor, which could significantly reduce the computational delay compared to traditional software approaches. In this project, the hardware processor is first designed and verified using Cadence software in CMOS (Complementary Metal Oxide Semiconductor) IBM 0.13μm technology, and then mapped to the Xilinx Spartan-3A DSP Development Board for quick implementation. Test results have shown the hardware processor functions correctly with sequences of traffic images. We also design the algorithms to post-process the motion vectors output from the hardware processor to complete the overall vehicle tracking process.
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    Feasibility Study on Development of a CMOS Vision Processor for Vehicle Tracking
    (2007-03) Tang, Hua
    Vehicle tracking is an important area of intelligent transportation systems (ITS) technology, which could be applied in a wide range of transportation applications. Tracking typically needs to monitor real-time vehicle movements, and thus real time tracking is highly desirable. However it is well known that vehicle tracking processes are computationally very intensive. In the past, regardless of different algorithms employed in vehicle tracking, they have been implemented using software approaches, e.g., FPGA (Field Programmable Gate Array), microcontroller or embedded micro-processor, and PCs. While software approaches have an advantage of flexibility in implementation and future modifications, its long computational time often prevents real-time vehicle tracking from high resolution spatial or temporal data. It is well known in the area of VLSI (Very Large Scale Integrated) circuit design that a customized and dedicated hardware implementation of any algorithm minimizes its computational time. This gives us the motivation for direct implementation of tracking algorithms in hardware (i.e., device level), whether it is a partial or full implementation, to enhance real-time operation. The goal of this seed project is to investigate the feasibility and related issues in developing a tracking system with a new tracking algorithm based on vehicle motion detection, which is implemented in hardware whenever possible so that the computational time for tracking is minimized. The proposed overall tracking system consists of two parts. One part is the hardware, more specifically, a CMOS (Complementary Metal Oxide Semiconductor) hardware processor which is mainly responsible for vehicle motion detection. The other part is the software, for example an FPGA or micro-controller which is responsible for analyzing the data transmitted from the hardware and properly associating vehicles for tracking. The main computational time saving for the tracking process comes from the hardware part since the core of the new tracking algorithm, motion detection, is run on a dedicated hardware for that particular purpose. The proposed tracking algorithm is simulated in MATLAB and tested on traffic images captured from an intersection. It is found that vehicle movements can be accurately identified in spite of some noisy motion. Also, in this project, we estimate the computational time for the tracking algorithm in hardware implementation and discuss high-level hardware designs for actual implementation of the tracking algorithm.
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    A Tracking-Based Traffic Performance Measurement System for Roundabouts and Intersections
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-05) Tang, Hua; Dinh, Hai
    Automatic traffic data collection can significantly save labor work and cost compared to manual data collection. The collected traffic data is necessary for traffic simulation and modeling, performance evaluation of the traffic scene, and eventually (re)design of the traffic scene. However, automatic traffic data collection has been one of the challenges in Intelligent Transportation Systems (ITS). This project presents the development of a single camerabased video system for automatic traffic data collection for roundabouts and intersections. The system targets roundabouts and intersections because no mature data collection systems exist for these traffic scenes yet in contrast to highway scenes. The developed system has mainly processing modules. First, the camera is calibrated for the traffic scene of interest and a novel circle-based calibration algorithm is proposed for roundabouts. Second, the system tracks vehicles from the video by incorporating powerful imaging processing techniques and tracking algorithms. Finally, the resulting vehicle trajectories from vehicle tracking are analyzed to extract the interested traffic data, which includes vehicle volume, vehicle speed (including acceleration/de-acceleration behavior), travel time, rejected gaps, accepted gaps, follow-up time and lane use. Practical tests of the developed system show that it can reliably track vehicles and provide reasonably accurate traffic data in most cases.