应用于交通监控的智能相机设计

智能相机是一种把先进的具有高性能处理功能的CMOS图像传感器集成到一个嵌入式系统中,它具有信号检测、信号处理和通信等功能.本文介绍了一款用于交通监控的智能相机,给出了具体的实现方法.对实现过程中的关键问题进行了分析并给出了解决方案,实现了用于交通监控的智能相机的样机.本文设计的智能相机可以获取并处理交通视频信号,还可以...     

Future demand for air traffic services

Forecasts of the demand for air transportation indicate continued growth in that demand throughout the next 25 years. This paper translates those demand forecasts into measures of air traffic activity--flights, airport operations, and numbers of aircraft airborne at peak periods. Total air traffic activity in this country is projected to increase four-fold between 1968 and 1995. Most aircraft flying in the 1990's are expected to be provided some sort of control service; to serve these users, the capacity of the en route instrument flight-rules system may need to be increased by a factor of about eight. Providing air traffic control services to local and itinerant flights operating to and from all airports within approximately a 30-mile radius of a major terminal might increase tower (or computer) control loads to 10 or 15 times today's peaks.     

适用于实时交通监控的时空可伸缩视频编码方法

提出了一种适用于视频监控的时空可伸缩编码方法。首先提出一种适用于可伸缩视频编码特点的监控算法,利用该算法将运动区域从背景中提取出来,并用模式和方向快速判别算法去掉对编码增益很小的冗余模式,基本层利用该算法进行编码,增强层利用基本层的信息预测可能的模式集合,然后进行零块预先判决,最后对最高时间级的高速运动块通过视觉门限来降低其空间冗余度。实验证明,该方法能够较大地提高编码速度和一定程度上提高压缩率,并且视觉质量损失很小。     

Freeway traffic surveillance and control

In the past decade, automatic freeway surveillance and control systems have been introduced in several cities in the United States. Effective use of the hardware that has been implemented requires a sound mathematical basis for evaluation and optimization studies. In this paper, the nature of freeway congestion and the structure of systems which have been implemented to relieve this congestion are discussed. Mathematical models of freeway traffic which allow consideration of on-ramp metering control are developed. Finally, several freeway traffic-control problems are discussed, current approaches are described, and formulations of improved (optimal) approaches are developed.     

IMM4HT:an identification method of malicious mirror website for high-speed network traffic

Aiming at the problem that some information causing harm to the network environment was transmitted through the mirror website so as to bypass the detection,an identification method of malicious mirror website for high-speed network traffic was proposed.At first,fragmented data from the traffic was extracted,and the source code of the webpage was restored.Next,a standardized processing module was utilized to improve the accuracy.Additionally,the source code of the webpage was divided into blocks,and the hash value of each block was calculated by the simhash algorithm.Therefore,the simhash value of the webpage source codes was obtained,and the similarity between the webpage source codes was calculated by the Hamming distance.The page snapshot was then taken and SIFT feature points were extracted.The perceptual hash value was obtained by clustering analysis and mapping processing.Finally,the similarity of webpages was calculated by the perceptual hash values.Experiments under real traffic show that the accuracy of the method is 93.42%,the recall rate is 90.20%,the F value is 0.92,and the processing delay is 20 μs.Through the proposed method,malicious mirror website can be effectively detected in the high-speed network traffic environment.     

Queueing performance estimation for general multifractal traffic

We present an approximation for the tail asymptotics in an infinite capacity single server queue serviced at a constant rate driven by general multifractal input process. We show that in the special and important case of the monofractal fractional Brownian motion input traffic our result gives the well‐known Weibullian tail. We prove that the class of Gaussian processes with scaling properties is in the class of monofractal processes and we derive the related characterization functions. Our formula in the case of Gaussian input processes also gives a queueing result which is in good agreement with the theory of Gaussian processes. Applying the approximation we provide a new practical method for queueing performance estimation of general multifractal traffic. The validation of the method based on both analysis of simulations and measured network traffic have also been presented. Copyright © 2003 John Wiley & Sons, Ltd.     

Beacon target processing for air traffic control

The beacon subsystem is one of many sources providing information to the air traffic control system. In automatic systems, various types of target information are derived from the beacon replies in a special purpose computer known generally as a processor. An important aspect of processor performance is its ability to detect targets in a noisy environment and to accurately determine the location of these targets. Among the important measures of target detector performance are the false alarm rate, the probability of detection, and azimuth accuracy. The intimate relationship between these three characteristics is described in this paper and specific examples are given using a sequential observer target detector. It is shown that increasing the range resolution of the system also benefits target false alarm rate and azimuth accuracy. Thus, as advances in technology permit improved range resolution, greater overall system performance can be expected. Nevertheless, with current technology, the azimuth accuracy which can be achieved with a statistical detector is better than the azimuth equivalent of one pulse repetition period even in a noisy environment.     

Fast motion estimation for surveillance video compression

In this article, novel approaches to perform efficient motion estimation specific to surveillance video compression are proposed. These includes (i) selective (ii) tracker-based and (iii) multi-frame-based motion estimation. In selective approach, motion vector search is performed for only those frames that contain some motion activity. In another approach, contrary to performing motion estimation on the encoder side, motion vectors are calculated using information of a surveillance video tracker. This approach is quicker but for some scenarios it degrades the visual perception of the video compared with selective approach. In an effort to speed up multi-frame motion estimation, we propose a fast multiple reference frames-based motion estimation technique for surveillance videos. Experimental evaluation shows that significant reduction in computational complexity can be achieved by applying the proposed strategies.     

Highway traffic surveillance and control research

The Burean of Public Roads of the U. S. Department of Transportation, through the Traffic Systems Division of its Office of Research and Development, is conducting a major research program in the fields of highway traffic surveillance and control. The effort involves in-house, coatract, and cooperative Federal-State research. Being given individual as well as integrated systems consideration are urban highway networks, freeways and rural multilane highways, and two-lane rural highways. Among the many projects trader way or being planned are several "major emphasis" projects, offering particularly great potential for rapid payoff, together with related theoretical research. These include: 1) an urban traffic control system, utilizing a digital computer, for research into advanced signal control concepts 2) a refined ramp-freeway merging control system 3) advanced freeway flow modelling and surveillance techniques and possible automation of flow 4) a passing aid system for two-lane rural highways, to permit passing on roads with restricted sight distances 5) an electronic route guidance system to supplement signs 6) use of cooperative passing motorists to advise of drivers needing help 7) overall systems analysis of highway communications. Also under way is a broad long-range analysis of the function of transportation.     

A learning-based approach for leaf detection in traffic surveillance video

Multidimensional Systems and Signal Processing - Traffic surveillance video is recorded in uncontrolled outdoor scenarios. If the camera view gets obstructed by the leaves, the video will fail to...     

VoIP-based air traffic controller training

Extending VoIP beyond Internet telephony, we propose a case study of applying the technology outside of its intended domain to solve a real-world problem. This work is an attempt to understand an analog hardwired communication system of the U.S. Federal Aviation Administration, and effectively translate it into a generic standards-based VoIP system that runs on their existing data network. We develop insights into the air traffic training and weigh in on the design choices for building a soft real-time data communication system. We also share our real world deployment and maintenance experiences, as the FAA Academy has been successfully using this VoIP system in six training rooms since early 2006 to train the future air traffic controllers of the U.S. and the world.     

Hampton roads traffic surveillance and control system

A surveillance and control system was designed and implemented for the Hampton Roads Bridge-Tunnel Crossing connecting Hampton and Norfolk, VA. The facility is a part of the Interstate 64 subsystem and consists of two bridges at each end connected by two two-lane tunnels. This system has been operating successfully since November 15, 1977. The system provides the means for improving vehicular throughput and reducing congestion, improving the management of vehicle incidents and facility operations, improving motorist information, improving environmental conditions, and improving traffic data collection. A control room situated in one of the four tunnel ventilation buildings is the nucleus of the traffic management activities. The system enables vehicle flow control of tunnel access; incident detection, incident verification, and incident operations management; automatic response to environmental and overheight problems; hardware monitoring of the signs, signals, and vehicle detectors; execution of major traffic operations on the faculty upon operator request; and daily reporting and logging of system events. Vehicle data are collected and accumulated by the system and are used for reporting and for performing incident detection and access control.     

The effect of airspace loading by controlled air traffic on the air traffic control system

The purpose of this paper is to examine briefly the amount of traffic which air controllers can manage with varying levels of automation. It is shown that automated aids to the controller expand his capacity to handle traffic by a factor of 2-3. The use of area navigation and rerouting can increase his traffic handling capacity by another factor of 2-3. Application of both techniques will be necessary eventually to control the traffic forecasted for the 1990's. The contents of this paper are based in part upon a study performed for the DOTATC Advisory Committee entitled "New York airspace loading and configuration" [1].     

Spatial traffic estimation and characterization for mobilecommunication network design

This paper presents a new method for the estimation and characterization of the expected teletraffic in mobile communication networks. The method considers the teletraffic from the network viewpoint. The traffic estimation is based on the geographic traffic model, which obeys the geographical and demographical factors for the demand for mobile communication services. For the spatial teletraffic characterization, a novel representation technique is introduced which uses the notion of discrete demand nodes. We show how the information in geographical information systems can be used to estimate the teletraffic demand in an early phase of the network design process. Additionally, we outline how the discrete demand node representation facilitates the application of demand-based automatic mobile network design algorithms     

Method for traffic and blocking estimation from test-calling measurements

A method for the estimation of real traffic offered to a trunk from the congestion loss measured with a test-call generator is described. The method consists in simulating both kinds of traffic (real and artificial) and in extracting a relation between the above quantities. Simulations were made for several offered traffic models and test calling rates and led to the construction of tables and diagrams for practical use.     

Distance estimation from image defocus for video surveillance systems

When dealing with depth recovery, the estimation of depth from defocus information provides an alternative method to stereo vision. A new depth from defocus method is described that needs only an estimation of the background together with the image of the object of interest, instead of the two images which traditional depth from defocus algorithms require.     

TANA—An operating surveillance system for highway traffic control

A complete surveillance and traffic control electronic system was designed and is being put into operation in a newly built urban tollway in Naples, Italy. In this system, named TANA after the tollway name (TAngenziale di NApoli), information is collected from several hundred loop detectors placed along the highway (on each lane every 250 m) and is relayed to a Central Control Room, where it is processed in real time by a digital computer. As a result, traffic conditions are continuously displayed to operators, and useful information is given to motorists through automatically steered variable-message signs, which on the tollway display conditions of traffic ahead, conditions of the roadway, state of traffic on the exit ramps, and outside the tollway, information on the state of entrance ramps and on the conditions of traffic in given sections of the tollway. Strategies for the automatic incident detection carried out by the system are described. The main purposes of TANA system are the achievement of maximum safety in critical points such as tunnels and entrance ramps (where access is controlled by means of automatically steered tunnel controllers and ramp controllers), the decrease in number and importance of multiple accidents, the optimization of traffic flow in the tollway and in the interchanges with the urban network, and the possibility for the users to take educated decisions based on the knowledge of traffic conditions. Safety features are assured even in case of a fault in the central computer or in the telecommunications system, by means of local analog devices, operating in standby.     

Toward a deep CNN and RS-GOA framework for vehicle detection,traffic flow estimation,and optimal path selection from surveillance videos

Several autonomous traffic monitoring systems have been created as a result of the growing number of vehicles in urban areas. Traffic surveillance systems that use roadside cameras, in particular, are becoming widely used for traffic management. For an efficient traffic control and vehicle navigation system, accurate traffic flow information must be obtained based on the vehicles detected in surveillance videos. However, vehicles of various scales are difficult to spot in traffic surveillance videos due to the presence of barricades, other vehicles, and the impact of poor lighting. Also, adverse weather conditions like snow, fog, and heavy rain diminish the visual quality of the surveillance footage. This paper proposes multi-scale dense nested deep CNN (MSDN-DCNN) and regional search grasshopper optimization algorithm (RS-GOA) framework to accurately detect the vehicles, estimate the traffic flow, and find the optimal path with less travel time. First, the surveillance videos are pre-processed, which includes frame conversion, redundancy removal, and image enhancement. The pre-processed frames are given as input to the MSDN-DCNN for multi-scale vehicle detection. The detected results are used for vehicle counting and estimating the traffic flow. Finally, the optimal path is chosen based on the traffic flow information by using the RS-GOA algorithm. The performance of the proposed method is compared with the existing vehicle detection and path selection techniques. The results illustrate that the proposed Deep CNN-RS-GOA framework has improved performance with high detection accuracy (91.03%), high speed (53.9 fps), less running time (1,000 ms), less travel time, and faster convergence.