Adaptive video-based vehicle classification technique for monitoring traffic : [executive summary].

This report presents a methodology for extracting two vehicle features, vehicle length and number of axles in order to classify the vehicles from video, based on Federal Highway Administration (FHWA)’s recommended vehicle classification scheme. There are two stages regarding this classification. The first stage is the general classification that basically classifies vehicles into 4 categories or bins based on the vehicle length (i.e., 4-Bin length-based vehicle classification). The second stage is the axle-based group classification that classifies vehicles in more detailed classes of vehicles such as car, van, buses, based on the number of axles. The Rapid Video-based Vehicle Identification System (RVIS) model is developed based on image processing technique to enable identifying the number of vehicle axles. Also, it is capable of tackling group classification of vehicles that are defined by axles and vehicle length based on the FHWA’s vehicle classification scheme and standard lengths of 13 categorized vehicles. The RVIS model is tested with sample video data obtained on a segment of I-275 in the Cincinnati area, Ohio. The evaluation result shows a better 4-Bin length–based classification than the axle-based group classification. There may be two reasons. First, when a vehicle gets misclassified in 4-Bin classification, it will definitely be misclassified in axle-based group classification. The error of the 4-Bin classification will propagate to the axle-based group classification. Second, there may be some noises in the process of finding the tires and number of tires. The project result provides solid basis for integrating the RVIS that is particularly applicable to light traffic condition and the Vehicle Video-Capture Data Collector (VEVID), a semi-automatic tool to be particularly applicable to heavy traffic conditions, into a “hybrid” system in the future. Detailed framework and operation scheme for such an integration effort is provided in the project report.

[1]  M Hallenbeck,et al.  EQUIPMENT FOR COLLECTING TRAFFIC LOAD DATA , 2004 .

[2]  B Coifman.  An Assessment of Loop Detector and RTMS Performance , 2004 .

[3]  Heng Wei,et al.  Artificial neural network method for length-based vehicle classification using single-loop outputs , 2006 .

[4]  James F. Frenze.  A VIDEO-BASED METHOD FOR THE DETECTION OF TRUCK AXLES , 2006 .

[5]  Heng Wei,et al.  Observation-Based Study of Intersection Dilemma Zone Natures , 2009 .

[6]  Heng Wei,et al.  Quantifying Dynamic Factors Contributing to Dilemma Zone at High-Speed Signalized Intersections , 2011 .

[7]  Kyungnam Kim,et al.  Algorithms and evaluation for object detection and tracking in computer vision , 2005 .

[8]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[9]  Heng Wei,et al.  Characterize dynamic dilemma zone and minimize its effect at signalized intersections. , 2008 .

[10]  Guohui Zhang,et al.  Video-Based Vehicle Detection and Classification System for Real-Time Traffic Data Collection Using Uncalibrated Video Cameras , 2007, Transportation Research Record: Journal of the Transportation Research Board.

[11]  Gary R. Bradski,et al.  Learning OpenCV - computer vision with the OpenCV library: software that sees , 2008 .

[12]  Christian Micheloni,et al.  ADVANCED VISUAL-BASED TRAFFIC MONITORING SYSTEMS FOR INCREASING SAFETY IN ROAD TRANSPORTATION , 2003 .

[13]  Stefano Messelodi,et al.  A computer vision system for the detection and classification of vehicles at urban road intersections , 2005, Pattern Analysis and Applications.

[14]  Terrance E. Boult,et al.  Frame-rate omnidirectional surveillance and tracking of camouflaged and occluded targets , 1999, Proceedings Second IEEE Workshop on Visual Surveillance (VS'99) (Cat. No.98-89223).

[15]  Heng Wei,et al.  Length-based Vehicle Classification Models using Dual-loop Data against Stop-and-Go Traffic Flow , 2011 .

[16]  Benjamin Coifman,et al.  Speed Estimation and Length Based Vehicle Classification from Freeway Single Loop Detectors , 2009 .

[17]  Heng Wei,et al.  Developing Operating Mode Distribution Inputs for MOVES with a Computer Vision–Based Vehicle Data Collector , 2013 .

[18]  S. T. Birchfield,et al.  Vision-based detection, tracking and classification of vehicles using stable features with automatic camera calibration , 2008 .

[19]  Sergio A. Velastin,et al.  Vehicle localisation and classification in urban CCTV streams , 2009 .

[20]  Panos D. Prevedouros,et al.  Evaluation of Autoscope, SmartSensor HD, and Infra-Red Traffic Logger for Vehicle Classification , 2010 .

[21]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Michael J. Cassidy,et al.  Vehicle reidentification and travel time measurement on congested freeways , 2002 .

[23]  Heng Wei,et al.  Optimal Loop Placement and Models for Length-Based Vehicle Classification and Stop-and-Go Traffic , 2011 .

[24]  Trevor Darrell,et al.  Background estimation and removal based on range and color , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[25]  Stephen G. Ritchie,et al.  Real-Time Vehicle Classification Using Inductive Loop Signature Data , 2008 .

[26]  Jun-Wei Hsieh,et al.  Automatic traffic surveillance system for vehicle tracking and classification , 2006, IEEE Transactions on Intelligent Transportation Systems.

[27]  Ho Lee,et al.  Validating the Performance of Vehicle Classification Stations , 2012 .

[28]  W. Eric L. Grimson,et al.  Edge-based rich representation for vehicle classification , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[29]  Qiang Li,et al.  Observation-Based Lane-Vehicle Assignment Hierarchy: Microscopic Simulation on Urban Street Network , 2000 .

[30]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[31]  Timur Mauga.  Development of Florida Length Based Vehicle Classification Scheme Using Support Vector Machines , 2006 .

[32]  Michalis E. Zervakis,et al.  A survey of video processing techniques for traffic applications , 2003, Image Vis. Comput..

[33]  Rafael C. González,et al.  Digital image processing, 3rd Edition , 2008 .

[34]  A. Verri,et al.  A compact algorithm for rectification of stereo pairs , 2000 .

[35]  Joe Lee,et al.  Characterizing and Modeling Observed Lane-Changing Behavior: Lane-Vehicle-Based Microscopic Simulation on Urban Street Network , 2000 .

[36]  Benjamin Coifman,et al.  Using Dual Loop Speed Traps To Identify Detector Errors , 1999 .

[37]  Heng Wei,et al.  CONGESTION SCENARIO-BASED VEHICLE CLASSIFICATION DETECTION MODELS BASED ON TRAFFIC FLOW CHARACTERISTICS AND OBSERVED EVENT DATA , 2015 .

[38]  Gary R. Bradski,et al.  Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library , 2016 .