Browsing by Subject "Tracking systems"

Now showing 1 - 4 of 4
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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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    Infrared Thermal Camera-Based Real-Time Identification and Tracking of Large Animals to Prevent Animal-Vehicle Collisions (AVCs) on Roadways
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-05) Zhou, Debao
    Animal vehicle collision (AVC) is constantly a major safety issue for the driving on roadways. It is estimated that there are over 35,000 AVCs yearly resulting in 3 to 11 deaths, over 400 personal injuries, and close to 4,000 reported property damages of $1,000 or more. This justifies the many attempts that have been tried to detect large animals on road. However, very little success has been achieved. To reduce the number of AVCs, this research used an infrared (IR) thermal imaging method to detect the presence of large animals and to track their locations so drivers could avoid AVCs. The system consists of an infrared-thermal-image grabbing and processing system and a motion control system to track the objects. By analyzing the infrared thermal images, the presence of deer in surrounding areas have been identified, and thus tracked. Since the IR thermal imaging is independent of visible light, the system can work both day and night, even in bad weather. The system can cover a circle area up to 1,000 feet in radius for the identification of an object the size of an adult human being.
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    A Novel Collision Avoidance System for a Bicycle
    (Center for Transportation Studies, University of Minnesota, 2018-04) Jeon, Woongsun; Rajamani, Rajesh
    This project focuses on development of a sensing and estimation system for a bicycle to accurately detect and track vehicles for two types of car-bicycle collisions. The two types of collisions considered are collisions from rear vehicles and collisions from right-turning vehicles at a traffic intersection. The collision detection system on a bicycle is required to be inexpensive, small and lightweight. Sensors that meet these constraints are utilized.To monitor side vehicles and detect danger from a right-turning car, a custom sonar sensor is developed. It consists of one ultrasonic transmitter and two receivers from which both the lateral distance and the orientation of the car can be obtained. A Kalman Filter-based vehicle tracking system that utilizes this custom sonar sensor is developed and implemented. Experimental results show that it can reliably differentiate between straight driving and turning cars. A warning can be provided in time to prevent a collision. For tracking rear vehicles, an inexpensive single-beam laser sensor is mounted on a rotationally controlled platform. The rotational orientation of the laser sensor needs to be actively controlled in real-time in order to continue to focus on a rear vehicle, as the vehicle’s lateral and longitudinal distances change. This tracking problem requires controlling the real-time angular position of the laser sensor without knowing the future trajectory of the vehicle. The challenge is addressed using a novel receding horizon framework for active control and an interacting multiple model framework for estimation. The features and benefits of this active sensing system are illustrated first using simulation results. Then, extensive experimental results are presented using an instrumented bicycle to show the performance of the system in detecting and tracking rear vehicles during both straight and turning maneuvers.
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    Thermal Image-Based Deer Detection to Reduce Accidents Due to Deer-Vehicle Collisions
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-01) Zhou, Debao
    Deer-vehicle collision (DVC) is one of the most serious traffic issues in the Unite States. To reduce DVCs, this research developed a system using a contour-based histogram of oriented gradients algorithm (CNT-HOG) to identify deer through the processing of images taken by thermographic cameras. The system is capable of detecting deer in low visibility. Two motors are applied to enlarge the detection range and make the system capable of tracking deer by providing two degrees of freedom. The main assumption in the CNT-HOG algorithm is that the deer are brighter than their background in a thermo image. The brighter areas are defined as the regions of interest, or ROIs. ROIs were identified based on the contours of brighter areas. HOG features were then collected and certain detection frameworks were applied to the image portions in the ROIs instead of the whole image. In the detection framework, a Linear Support Vector Machine classifier was applied to achieve identification. The system has been tested in various scenarios, such as a zoo and roadways in different weather conditions. The influence of the visible percentage of a deer body and the posture of a deer on detection accuracy has been measured. The results of the applications on roadside have shown that this system can achieve high detection accuracy (up to 100%) with fast computation speed (10 Hz). Achieving such a goal will help to decrease the occurrence of DVCs on roadsides.