Development and application of vehicle‐license number recognition system using real‐time image processing

Kanayama, Kenji; Fujikawa, Yoshimasa; Fujimoto, Koichi

doi:10.1002/scj.4690220106

Cited by 3 publications

(3 citation statements)

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“…(2) The method by recognition of the vehicle license number using image processing [1][2]. (3) The method by sending and receiving of the vehicle's ID using in-vehicle unit [3].…”

Section: Outlinementioning

confidence: 99%

Travel time measurement by vehicle sequence matching method - examination of evaluation method focused shape of vehicle -

Takahashi

Izumi

2007

SICE Annual Conference 2007

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We studied a method of identifying vehicles using vehicle height data obtained from ultrasonic vehicle detectors. We previously proposed vehicle matching based on vehicle sequences (two or more vehicles) instead of one-to-one matching, because many similar vehicles exist on the road. We developed a genetic algorithm (GA) method based on traffic characteristic, and we describe an improved method of evaluating vehicle sequences using typical values of shape of vehicle, that is to say, front elevation degree and rear elevation degree. We report that the accuracy of vehicle identification was improved according to experimental results.

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“…(2) The method by recognition of the vehicle license number using image processing [1][2]. (3) The method by sending and receiving of the vehicle's ID using in-vehicle unit [3].…”

Section: Outlinementioning

confidence: 99%

Travel time measurement by vehicle sequence matching method - examination of evaluation method focused shape of vehicle -

Takahashi

Izumi

2007

SICE Annual Conference 2007

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“…(Method 1) Measurement methods based on speed information obtained from detectors [1] (Method 2) Methods using vehicle signal recognition with image processing [2,3] (Method 3) Methods using sent and received signals for vehicle identification [4] (Method 4) Methods using probe cars [5] Method 1 is currently the most common, and on highways a method for travel time measurement based on the average speed information between intervals using a vehicle detector is already in use. On the one hand, an ordinary road requires ultrasonic vehicle detectors installed in many places, and so this method can be considered superior in terms of generality.…”

Section: Introductionmentioning

confidence: 99%

Application of genetic algorithm to travel time measurement using vehicle data provided from ultrasonic vehicle detectors

Takahashi¹,

Izumi²

2009

Elect Comm in Japan

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SUMMARYInformation on travel time is useful in improving traffic efficiency, such as reducing traffic congestion effectively. Travel time is computed from the passage time of vehicles observed at both the origin (upstream) and destination (downstream) points. For this purpose, we are studying a vehicle identification method using vehicle height data obtained from ultrasonic vehicle detectors. However, matching methods using only the vehicle height suffer from poor identification accuracy because many similar vehicles exist on the road. Therefore, we propose a method based on matching vehicle sequences (two or more vehicles). In addition, we develop this method using a Genetic Algorithm based on traffic characteristics.

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“…One application of tracking techniques based on images is accident detection [4,15]. Each sensor captures and measures one vehicle, and equipment that completely determines one vehicle by using the techniques for reading license plates has been under development for many years [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Vehicle recognition system in infrared images using IMAP parallel vision board

Kagesawa

Ueno

Ikeuchi

et al. 2003

Electron Comm Jpn Pt III

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SUMMARYRecently, research on Intelligent Transportation Systems (ITS) has advanced, and its practical application has expanded to the image applications of measuring the traffic flow and detecting unusual events. In this paper, we describe a more advanced vehicle model identification technology, that is, a technology for detecting a particular model (model B of company A). This method does not read license plates or involve communication between the vehicle and the road, but identifies the vehicle model by using only roadside equipment. This algorithm recognizes the model by voting based on the arrangement of local features and has the following characteristics: (1) Detection is possible even if the target vehicle is occluded; (2) There is no dependence on the traveling position of the target vehicle; and (3) The vehicle region does not have to be extracted from the input image. First, we verify the above advantages in outdoor experiments by confirming the ability of the method called the eigenwindow method to recognize a specific model in the traffic flow. Next, we report on the successful improvement in the processing speed without a drop in the recognition accuracy by slightly modifying the implementation and using an image processing board.

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Development and application of vehicle‐license number recognition system using real‐time image processing

Cited by 3 publications

References 0 publications

Travel time measurement by vehicle sequence matching method - examination of evaluation method focused shape of vehicle -

Travel time measurement by vehicle sequence matching method - examination of evaluation method focused shape of vehicle -

Application of genetic algorithm to travel time measurement using vehicle data provided from ultrasonic vehicle detectors

Vehicle recognition system in infrared images using IMAP parallel vision board

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