3 research outputs found
Traffic Surveillance and Automated Data Extraction from Aerial Video Using Computer Vision, Artificial Intelligence, and Probabilistic Approaches
In transportation engineering, sufficient, reliable, and diverse traffic data is necessary for effective planning, operations, research, and professional practice. Using aerial imagery to achieve traffic surveillance and collect traffic data is one of the feasible ways that is facilitated by the advances of technologies in many related areas. A great deal of aerial imagery datasets are currently available and more datasets are collected every day for various applications. It will be beneficial to make full and efficient use of the attribute rich imagery as a resource for valid and useful traffic data for many applications in transportation research and practice. In this dissertation, a traffic surveillance system that can collect valid and useful traffic data using quality-limited aerial imagery datasets with diverse characteristics is developed. Two novel approaches, which can achieve robust and accurate performance, are proposed and implemented for this system. The first one is a computer vision-based approach, which uses convolutional neural network (CNN) to detect vehicles in aerial imagery and uses features to track those detections. This approach is capable of detecting and tracking vehicles in the aerial imagery datasets with a very limited quality. Experimental results indicate the performance of this approach is very promising and it can achieve accurate measurements for macroscopic traffic data and is also potential for reliable microscopic traffic data. The second approach is a multiple hypothesis tracking (MHT) approach with innovative kinematics and appearance models (KAM). The implemented MHT module is designed to cooperate with the CNN module in order to extend and improve the vehicle tracking system. Experiments are designed based on a meticulously established synthetic vehicle detection datasets, originally induced scale-agonistic property of MHT, and comprehensively identified metrics for performance evaluation. The experimental results not only indicate that the performance of this approach can be very promising, but also provide solutions for some long-standing problems and reveal the impacts of frame rate, detection noise, and traffic configurations as well as the effects of vehicle appearance information on the performance. The experimental results of both approaches prove the feasibility of traffic surveillance and data collection by detecting and tracking vehicles in aerial video, and indicate the direction of further research as well as solutions to achieve satisfactory performance with existing aerial imagery datasets that have very limited quality and frame rates. This traffic surveillance system has the potential to be transformational in how large area traffic data is collected in the future. Such a system will be capable of achieving wide area traffic surveillance and extracting valid and useful traffic data from wide area aerial video captured with a single platfor
Vehicle detection and tracking in airborne videos by multi-motion layer analysis
Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are receiving more and more attention due to their advantages of high mobility, fast deployment and large surveillance scope. However, such systems are difficult to develop because of factors like UAV motion, scene complexity, and especially the partial occlusion of targets. To address these problems, a new framework of multi-motion layer analysis is proposed to detect and track moving vehicles in airborne platform. After motion layers are constructed, they are maintained over time for tracking vehicles. Most importantly, since the vehicle motion layers can be maintained even when the vehicles are only partially observed, the proposed method is robust to partial occlusion. Our experimental results showed that (1) compared with other previous algorithms, our method can achieve better performance in terms of higher detection rate and lower false positive rate; (2) it is more efficient and more robust to partial occlusion; (3) it is able to meet the demand of real time application due to its computational simplicity
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Design, Data Collection, and Driver Behavior Simulation for the Open- Mode Integrated Transportation System (OMITS)
With the remarkable increase in the population and number of vehicles, traffic has become a severe problem in most metropolitan areas. Traffic congestion has imposed tight constraints on economic growth, national security, and mobility of riders and goods. The open-mode integrated transportation system (OMITS) has been designed to improve the traffic condition of roadways by increasing the ridership of vehicles and optimizing transportation modes through smart services integrating emerging information communication technologies, big data management, social networking, and transportation management. Even a modest reduction in the number of vehicles on roadways will lead to a considerable cost savings in terms of time and money. Additionally the reduction in traffic jams will lead to a significant decrease in both gasoline consumption and greenhouse gas emissions.
As a result, novel transportation management is critical to reduce vehicle mileage in the peak time of the road network. The OMITS was proposed to enhance transportation services in respect to the following three aspects: optimization of the transportation modes by multimodal traveling assignment, dynamic routing and ridesharing service with advanced traveler information systems, and interactive user interface for social networking and traveling information. Therefore, the OMITS encompasses a broad range of advanced transportation research topics, say dynamic trip- match, transportation-mode optimization, traffic prediction, dynamic routing, and social network- based carpooling.
This dissertation will focus on a kernel part of the OMITS, namely traffic simulation and prediction based on data containing the distribution of vehicles and the road network configuration. A microscopic traffic simulation framework has been developed to take into account various traffic phenomena, such as traffic jams resulting from bottlenecking, incidents, and traffic flow shock waves. Four fundamental contributions of the present study are summarized as follows:
Firstly, an accurate and robust vehicle trajectory data collection method based on image data of unmanned aerial vehicle (UAV) has been presented, which can be used to rapidly and accurately acquire the real-time traffic conditions of the region of interest. Historically, a lack in the availability of trajectory data has posed a significant obstacle to the enhancement of microscopic simulation models. To overcome this obstacle, a UAV based vehicle trajectory data collection algorithm has been developed. This method extracts vehicle trajectory data from the UAV’s video at different altitudes with different view scopes. Compared with traditional methods, the present data collection algorithm incorporates many unique features to customize the vehicle and traffic flow, through which vehicle detection and tracking system accuracy can be considerably increased.
Secondly, an open mechanics-based acceleration model has been presented to simulate the longitudinal motion of vehicles, in which five general factors—namely the subject vehicle’s speed and acceleration sensitivity, safety consideration, relative speed sensitivity and gap reducing desire—have been identified to describe drivers’ preferences and the interactions between vehicles. Inspired by the similarity between vehicle interactions and particle interactions, a mechanical system with force elements has been introduced to quantify the vehicle’s acceleration. Accordingly, each of the aforementioned five factors are assumed to function as an individual trigger to alter each vehicle’s speed. Based on Newton’s second law of motion, the subject vehicle’s longitudinal behavior can be simulated by the present open mechanics-based acceleration model. By introducing feeling gap, multilane acceleration behavior is included in the presented model. The simulation results fit realistic conditions for the traffic flow and the road capacity very well, where traffic shockwaves can be observed for a certain range of the traffic density. This model can be extended to more general scenarios if other factors can be recognized and introduced into the modeling framework.
Thirdly, a driver decision-based lane change execution model has been developed to describe a vehicle’s lane change execution process, which includes two steps, i.e. driver’s lane selection and lane change execution. Currently, most lane change models focus on the driver’s lane selection, and overlook the driver’s behavior during a process of lane change execution which plays a significant role in the simulation of traffic flow characteristics. In this model, a lane change execution is analyzed as a driver’s decision-making process, which consists of desire point setting, priority decision-making, corresponding actions and achievement of consensus analysis.
Compared with the traditional lane change execution models, the present model describes a realistic lane change process, and it provides more accurate and detailed simulation results in the microscopic traffic simulation.
Based on the presented open mechanics-based acceleration model and the driver decision- based lane change execution model, a reverse lane change model has further been developed to simulate some complex traffic situations such as reverse lane change process at a two-way-two- lane road section where one lane is blocked by a traffic incident. Based on this reverse lane change model, information on the average waiting time and road capability can be obtained. The simulation results show that the present model is able to reflect real driver behavior and the corresponding traffic phenomenon during a reverse lane change process
Through a homogenization process of the microscopic vehicle motion, we can obtain the macroscopic traffic flow of the roadway network within certain time and spatial ranges, which will be integrated into the OMITS system for traffic prediction. The validation of the models through future OMITS operations will also enable them to be high fidelity models in future driverless technologies and autonomous vehicles