Implementation of raspberry Pi for vehicle tracking and travel time information system: A survey

Travel time is important information for traffic management system, which can help people to plan their travel schedule and improve their work efficiency. The development of smart travel time information system for multiple moving vehicle detection and tracking on highway composed of an embedded Linux platform and an image sensor. A low cost system with high resources is needed to capture an image of the monitoring area, analyze it and perform the vehicle detection and tracking process of the image to estimate the speed and time taken of moving vehicle from one point to another point on the scene. So, this paper will review some of embedded board that been used with image processing to find out which kind of platform that is suitable and possible to measure and estimate the travel time.

[1]  Xuesong Zhou,et al.  Dynamic Origin-Destination Demand Flow Estimation Utilizing Heterogeneous data sources under Congested Traffic Conditions , 2011 .

[2]  Shih-Chieh Huang,et al.  A Vision-Based Vehicle Speed Warning System , 2012, 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing.

[3]  M. Kalyan Chakravarthi,et al.  RASPBERRY - PI BASED COST EFFECTIVE VEHICLE COLLISION AVOIDANCE SYSTEM USING IMAGE PROCESSING , 2015 .

[4]  Lili Huang,et al.  A Novel Loglinear Model for Freeway Travel Time Prediction , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[5]  Jun-Wei Hsieh,et al.  An Automatic Traffic Surveillance System for Vehicle Tracking and Classification , 2003, SCIA.

[6]  Tereza Pultarova Computing - Raspberry PI 2 aims to give PC makers run for money , 2015 .

[7]  Rinu Merin Baby,et al.  Optical Flow Motion Detection on Raspberry Pi , 2014, 2014 Fourth International Conference on Advances in Computing and Communications.

[8]  Lelitha Vanajakshi,et al.  A model based approach to predict stream travel time using public transit as probes , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[9]  Chomtip Pornpanomchai,et al.  Vehicle speed detection system , 2009, 2009 IEEE International Conference on Signal and Image Processing Applications.

[10]  Ravindra C. Thool,et al.  Moving Vehicle Detection for Measuring Traffic Count Using OpenCV , 2013 .

[11]  Y. Tanaka Travel-time data provision system using vehicle license number recognition devices , 1992, Proceedings of the Intelligent Vehicles `92 Symposium.

[12]  Hiroki Yamazaki,et al.  The effect of a new intercity expressway based on travel time reliability using electronic toll collection data , 2012 .

[13]  Ashish Bhaskar,et al.  Bluetooth Vehicle Trajectory by Fusing Bluetooth and Loops: Motorway Travel Time Statistics , 2014, IEEE Transactions on Intelligent Transportation Systems.

[14]  Kun Zhang,et al.  Practical travel time prediction algorithms based on neural network and data fusion for urban expressway , 2010, 2010 Sixth International Conference on Natural Computation.

[15]  Michal Kochlán,et al.  WSN for traffic monitoring using Raspberry Pi board , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[16]  Asako Yumoto,et al.  Vehicle speed estimation using video data and acceleration information of a drive recorder , 2013, 2013 13th International Conference on ITS Telecommunications (ITST).

[17]  Elmer P. Dadios,et al.  Low cost smart security camera with night vision capability using Raspberry Pi and OpenCV , 2014, 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM).

[18]  Luc Van Gool,et al.  Dynamic 3D Scene Analysis from a Moving Vehicle , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Moustafa Youssef,et al.  RF-Based Vehicle Detection and Speed Estimation , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[20]  Saleh Basalamah,et al.  Vehicle Speed Estimation Using Wireless Sensor Network , 2011 .

[21]  Hwasoo Yeo,et al.  Travel time prediction for Origin-Destination pairs without route specification in urban network , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[22]  S. Bera,et al.  Estimation of origin-destination matrix from traffic counts: the state of the art , 2011 .

[23]  Rod Troutbeck,et al.  Travel Time and Origin-Destination Data Collection using Bluetooth MAC Address Readers , 2010 .

[24]  Ken Chen,et al.  A Meanshift-based imbedded computer vision system design for real-time target tracking , 2012, 2012 7th International Conference on Computer Science & Education (ICCSE).

[25]  A. Ambardekar Efficient vehicle tracking and classification for an automated traffic surveillance system , 2007 .

[26]  Manjunath Narayana,et al.  Automatic Tracking of Moving Objects in Video for Surveillance Applications , 2007 .

[27]  Rita Cucchiara,et al.  Energy-efficient feedback tracking on embedded smart cameras by hardware-level optimization , 2011, 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras.

[28]  Peng Sun,et al.  The Design of Embedded Video-Based Vehicle Tracking System , 2013, 2013 Fourth International Conference on Digital Manufacturing & Automation.

[29]  Lifeng Zhang,et al.  New Vehicle Speed Measurement System with Image Processing , 2014, ICIS 2014.

[30]  Martin Isaksson,et al.  Vehicle detection using anisotropic magnetoresistors , 2008 .

[31]  Romain Billot,et al.  Spatiotemporal Analysis of Bluetooth Data: Application to a Large Urban Network , 2015, IEEE Transactions on Intelligent Transportation Systems.

[32]  Qi Wang,et al.  Analysis of Travel Time Patterns in Urban Using Taxi GPS Data , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.