Traffic Control Model using Image Processing and Cloud Computing based online learning

Transcription

1 Traffic Control Model using Image Processing and Cloud Computing based online learning Ms. Sonali Rohilla Dept of Computer Science Mahamaya Technical University Ms. Alka Singhal Dept of Computer Science Mahamaya Technical Unversity Abstract Road congestion due to vehicle traffic is a recurring problem worldwide. In modern life we have to face with many problems one of which is traffic congestion becoming more serious day after day. The daily congestion on today s roads requires an innovative solution of traffic management systems. Through this paper, architecture is going to be presented for adapti ve traffic control system with online learning feature. Proposed architecture could provide Services such as autonomy, mobility, decision support and the standard development Environment for traffic management strategies, and so on. Cloud computing allow for an inexpensive use of mass quantities of storage, bandwidth and computing resources using the pay-per-use model on which it thrives. Once information is collected and analyzed further control measures are taken to reduce traffic. Keywords- Cloud computing, intelligent traffic system, Background extraction, Mobile device sensors, Video Image processing. I. INTRODUCTION Most of the traffic control signals are static systems, in which the traffic signal routine is pre-defined and played repetitively. However, real time stories had indicated that such static traffic control signals are non-optimal in various respects [1-4]. The direct outcome of the use of adaptive traffic signal control is the reduction in the vehicle s waiting time as well as the no-traffic time at the traffic signals [1]. However, besides this direct outcome, other important advantages by using adaptive traffic signal control are reduction in the emissions from vehicles, fuel consumption, congestions (specially on busy routes and in the peak traffic periods), number of stops along corridors, etc. [2-4]. There can be two major technologies, which can be us ed for adaptive traffic control systems that are microwave range Doppler radar based systems [7] and video based image processing system [1, 5-6, 8-9]. To use Doppler radar based systems is difficult for traffic signal control because the system will require multiple independent radio beams or channels for tracking each vehicle in the traffic system and implementing such systems may not be feasible,cumbersome and expensive too. One of the main requirements of the traffic signal control system is that such system should be able to process the data (camera images) in real time. Specified requirement requires that, most adaptive traffic signal control systems should use one basic block of vehicle detection [5-6, 8-9]. Sometimes, some additional simple image processing steps are also included. For example, simple background detection and updating is used in [1]. A scheme for lane detection, vehicle tracking and motion detection is incorporated in [8]. Though these simple approaches keep the system real time, the accuracy of such systems with simplistic vehicle detection techniques is comparatively low [8]. Video images processing requires to deal with various issues and addressing such issues directly improves the performance of vehicle detection algorithms and consequently improve the accuracy of such systems [1, 5-6, 8, 10-12]. The approaches can be image enhancements (contrast, hue correction, equalization, colour balance, etc.), shadow removal, background detection and updating, noise cancellation /compensation, image correction for illumination variation (sunny days, cloudy days, nights, etc.), weather variations (snowfall, after snowfall, rainy day, etc.), and so on. Another very important issue with the existing adaptive traffic signal control systems is that though there is control over the traffic signal according to the current traffic conditions, the system does not learn from its previous experiences or neither it update itself for forthcoming conditions that it encounters day by day that are increasing day by day. One way of addressing this issue is to maintain a history of traffic data by storing the data on the local system, send offline support system that collects this data, perform data analysis and machine learning and then update the system offline with newly learnt rules features, and semantics. Thus, the learning and data analysis happens online, not very frequently, and with significant delays after the actual data is gathered. If an online learning system should be implemented, it shall put heavy demands on the computational requirements of the system and entail significant increase in the processing time. Such adaptive systems are not themselves capable of being scaled so that the whole traffic network can be made adaptive. Page 28

2 Using GPS was one of the technologies used by a number of researchers to solve the traffic jam problem. The [13] work can be considered as an example when they have used GPS to detect the on road vehicles speed and control the traffic light to enhance. interaction can also be used to approximate the figure. Modes other than the video camera can be used in situations like poor weather conditions like foggy weather, rain etc. Figure: 1 Start II. PROBLEM DEFINITION Increased traffic congestion and associated pollution are forcing everyone in transportation to think about rapid changes in traffic processes and procedures to keep our mobility safe, comfortable, and economical. Video Frames Mobile towers Other modes Though, there has been lot of work done in the area of Traffic Control system through cloud computing. But there were always certain limitations with the system. The problems identified with the previous work done are: Video Processing Unit Physical location detected 1. Controlling the traffic from a central location by phasing traffic light fro m all around the city and then sending them to master controller through some communication media. The process sometimes results in delays and in turn congestion problems. 2. Using a camera alone to analyze traffic condition was not efficient enough as it would not work always like in heavy raining, sand storm conditions and other unfavorable weather conditions. 3. Using GPS has a limitation in terms service reception while driving inside a modern city which a lot of long buildings. Lack of high performance computing platform. 4. Systems were not scalable as they were designed in a centralistic way. III. PROPOSED MODEL Sense traffic data City traffic center Country traffic center Intellige nt traffic cloud services CLOUD SERVER The system operation is shown as a flowchart in Figure 1. For better coverage of receiving traffic information three sources are used video cameras, mobile device detectors and other modes. For avoidance of traditional traffic control system limitation the communication network will exist so that it will be accountable to a series of on-demand service, except that result of the calculations will be send to both, the vehicle and control centers. Traffic knowledge Application & Services The source video camera is capturing the videos from roads and pre-processor unit attached with the system is counting the number of vehicles and sending the data to city traffic center. The model also proposes to use mobile device detectors to approximate the number of vehicles in a particular region and other modes like human key Traffic manager Display Boards Mobile SMS Traffic light controller Page 29

3 Control center packed the requests and send them to which are provider of computational capacity and storage of control center, then the results after processing resend to the center control. Traffic control centers in each city is subsidiaries of the country's traffic control center and information will be send to the database of country traffic center and at the same time will archive in database (For more cautiously and according to the needs, the history of the Traffic Control Center are stored). Each country's traffic control centers are connected together by using the cloud server environment. In this mode, once the sending information from city's traffic control centers to country's traffic control center this data in cloud application have been updated in environment that cloud make it possible and with using the related graphic supported these data becomes the visual map in graphics form. This map will become global as soon as the traffic information post will be updated. The so obtained resultant will support all features and route of roads. Figure 2 A. Video Processing Unit Video processing systems constitutes a stream processing architecture, in which video frames from a continuous stream are processed one (or more) at a time. This type of processing is critical in systems that have live video or where the video data is so large that loading the entire set into the workspace is inefficient. There are three types of methods mainly used in detection of moving object in video processing: 1.Frame subtraction method[15] 2.Optical Flow Method[14] 3.Background subtraction method[16] The difference between two consecutive images is taken to determine the presence of moving objects. The calculation in this method is very simple and easy to develop. But in this method it is difficult to obtain a complete outline of moving object; therefore the detection of moving object is not accurate. Calculation of the image optical flow field is done. The clustering processing is done according to the optical flow distribution characteristics of image. From this, the complete movement information of moving body is found and it detects the moving object from the background. The method in which the difference between the current image and background image is taken for the detection moving objects by using simple algorithm. But it is very sensitive to the changes which occur in the external environment and it also has poor anti interference ability. B. Cloud computing Intelligence traffic cloud services will help out in creating such maps and will provide required traffic knowledge to control and transmit control signals to the traffic light controller, SMS to the mobile device users to change their route and display boards on roads can also be used to show the maps with information to the commuters to take right decisions to avoid traffic jams and time delays in their arrivals. Cloud compuing is internet-based computing in which large groups of remote servers are networked to allow sharing of data-processing tasks, centralized data storage, and online access to computer services or resources. Clouds can be classified as public, private or hybrid [17]. Page 30

4 Figure: 3 IV. CONCLUSION Increment in traffic congestion and the associated pollution problems are forcing everyone in transportation to think about rapid changes in traffic processes and procedures to keep our mobility safe, comfortable, and economical. The paper present an extended architecture for traffic control system that uses image processing, mobile sensing and online learning with the help of cloud computing. A cloud computing based architecture is also proposed which has many advantages over local only system. Advantages like reduced demand on computation resources, increased accuracy, reduced computation time, and increased frame rate are reported. Other advantages like scalability and robustness are also discussed. In our opinion, such architecture can be deployed at a very small cost, as the current computation resources at local systems may be sufficient for cloud computing architecture. Further, the system can be easily scaled, maintained, and updated through the cloud servers, providing significant improvement in the current traffic scenario. IV. REFERENCES Three main types of cloud services: 1. Software as a Service (SaaS) This service provides end-user applications running on a cloud infrastructure that can be accessible from various client devices. Examples of such applications include accounting, collaboration, customer relationship management (CRM), enterprise resource planning (ERP), invoicing, human resource management (HRM), content management (CM) and service desk management services, etc. 2. Platform as a Service (PaaS) This service facilities for application design / development, testing, deployment and hosting as well as platform services for team collaboration, web service integration and marshalling, database integration and developer community facilitation, etc. 3. Infrastructure as a Service (IaaS) This service provides processing, storage, networks, and other fundamental computing resources where the consumers are able to deploy and run their own software. Examples of such services include storage, computation, content delivery network (CDN), service management and etc. [1] L. Y. Deng, N. C. Tang, D. L. Lee, C. T. Wang, and M. C. Lu, "Vision based adaptive traffic signal control system development," in International Conference on Advanced Information Networking and Applications, 2005, Los Alamitos, 2005, pp [2] D. Associates, "Evaluation of an adaptive traffic signal system," DKS Associates, Oakland, USA2010. [3] T. T. Consultants, "Evaluation of main street adaptive traffic signal system," TJKM Transportation Consultants, Pleasanton, USA2011. [4] J. M. Hutton, C. D. Bokenkroger, and M. M. Meyer, "Evaluation of an adaptive traffic signal system: route 291 in Lee s summit, Missouri," Midwest Research Institute and Missouri Department of Transportation, Kansas City, USA2010. [5] A. L. P. Douglas, M. Prasad, S. Gowtham, A. Kalyansundar, V. Swaminathan, and R. Chattopadhyay, "An efficient DSP Page 31

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