Paul W. Shuldiner

DETERMINING DETAILED ORIGIN-DESTINATION AND TRAVEL TIME PATTERNS USING VIDEO AND MACHINE VISION LICENSE PLATE MATCHING

Examples are presented of the use of machine vision technology for reading and processing vehicle license plate images acquired in the field by video camcorders. Applications include travel time and microscale origin-destination pattern analyses for a freeway interchange in Tampa and the road network serving Houstons Intercontinental Airport, and high-occupancy vehicle lane performance measurements in Seattle. It is concluded that video and machine vision analysis of license plates is an effective and efficient means of conducting a wide variety of traffic engineering and traffic management studies.

Effects of Ambient Light, Camcorder Settings, and Automated License Plate Reader Settings on Plate Transcription Rates

The ability of an automated license plate reading (ALPR) system to convert video images of license plates into computer records depends on many factors. Of these, two are readily controlled by the operator: the quality of the video images captured in the field and the internal settings of the ALPR used to transcribe these images. A third factor, the light conditions under which the license plate images are acquired, is less easily managed, especially when camcorders are used in the field under ambient light conditions. A set of experiments was conducted to test the effects of ambient light conditions, video camcorder adjustments, and internal ALPR settings on the percent of correct reads attained by a specific type of ALPR, one whose optical character recognition process is based on template matching. Images of rear license plates were collected under four ambient light conditions: overcast with no shadows, and full sunlight with the sun in front of the camcorder, behind the camcorder, and orthogonal to the line of sight. Three camcorder exposure settings were tested. Two of the settings made use of the camcorder’s internal light meter, and the third relied solely on operator judgment. The license plates read ranged from 41% to 72%, depending most strongly on ambient light conditions. In all cases, careful adjustment of the ALPR led to significantly improved read rates over those obtained by using the manufacturer’s recommended default settings. Exposure settings based on the operator’s judgment worked best in all instances.

Real-Time License Plate Reading for Origin/Destination Studies

Determination of vehicle origins and destinations have long been a goal and a tool for traffic planners. Recently there has been an increase in the use of machine vision systems (automatic license plate readers) for determining origins and destinations. The original work in this area involved recording traffic on s-video compatible tape formats (e.g. s-VHS and Hi-8mm). Recently the use of real time analysis of traffic flows has been implemented. This paper describes the implementation of a system to acquire license plate information on the Tacoma Narrows Bridge, in Washington state. The paper describes the system installation, the system architecture, the data handling and mail outs and some preliminary results.

Application of video/machine vision technology in traffic data analysis

Measurement of travel times provides a critical component, along with traffic volumes, in the calculation of traffic congestion. This paper describes the results of extensive field tests of several methods used to calculate travel times. In particular, the paper examines the use of Hi- 8 mm video cameras (camcorders) and an automatic license plate reading system, based on machine vision technology, to perform travel time calculations. The paper presents the preliminary results of field trials conducted in Boston, Mass., Seattle, Wash., and Lexington, Kentucky. Besides the results of the surveys, the paper examines issues related to transferring this new technology to this application. The paper looks at the logistics of conducting extensive surveys, lessons applicable to survey design, and the quantity and statistical significance of the data required. A comparison is made between the application of machine vision to traffic surveys with other applications of machine vision to the transportation industry. It also presents future plans for the use of machine vision for the measurement of various traffic parameters.