EP2320384B1 - Verfahren zur Kennzeichenerkennung und Mautkategorisierung von Fahrzeugen - Google Patents

Verfahren zur Kennzeichenerkennung und Mautkategorisierung von Fahrzeugen Download PDF

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Publication number
EP2320384B1
EP2320384B1 EP09450175A EP09450175A EP2320384B1 EP 2320384 B1 EP2320384 B1 EP 2320384B1 EP 09450175 A EP09450175 A EP 09450175A EP 09450175 A EP09450175 A EP 09450175A EP 2320384 B1 EP2320384 B1 EP 2320384B1
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EP
European Patent Office
Prior art keywords
vehicle
fingerprint
licence plate
ocr
ref
Prior art date
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Active
Application number
EP09450175A
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German (de)
English (en)
French (fr)
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EP2320384A1 (de
Inventor
Christian Öhreneder
Herbert Ramoser
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Kapsch TrafficCom AG
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Kapsch TrafficCom AG
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Filing date
Publication date
Application filed by Kapsch TrafficCom AG filed Critical Kapsch TrafficCom AG
Priority to ES09450175T priority Critical patent/ES2401324T3/es
Priority to EP09450175A priority patent/EP2320384B1/de
Priority to DK09450175.6T priority patent/DK2320384T3/da
Priority to PL09450175T priority patent/PL2320384T3/pl
Priority to SI200930504T priority patent/SI2320384T1/sl
Priority to PT94501756T priority patent/PT2320384E/pt
Publication of EP2320384A1 publication Critical patent/EP2320384A1/de
Application granted granted Critical
Publication of EP2320384B1 publication Critical patent/EP2320384B1/de
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
    • G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
    • G07B15/06—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles

Definitions

  • the present invention relates to a method for automatic license plate recognition of a vehicle, in particular for tolling purposes in road toll systems according to the preamble of claim 1.
  • a method for automatic license plate recognition of a vehicle in particular for tolling purposes in road toll systems according to the preamble of claim 1.
  • Such a method is known from US 2007/008179 A1 known.
  • Road toll systems based on automatic license plate recognition by image capturing the vehicle are also referred to as video toll systems.
  • the vehicles are taken when passing a toll collection station with one or more cameras.
  • the cameras record a front and / or rear view of the vehicle in which the license plate is as clear as possible.
  • OCR automatic character recognition
  • a vehicle account with the vehicle registration number is maintained for each vehicle.
  • the vehicle passage is assigned to the vehicle account whose license plate matches the OCR license plate reading result.
  • a problem with the known methods is the high error rate of the OCR number plate recognition.
  • the incorrectly read license plates must be corrected manually and assigned to a vehicle account. In the worst case, it can happen that a wrongly read license plate confused with a license plate of another vehicle and this vehicle account is charged with the passage.
  • fingerprints are calculated from images taken in different light and weather conditions, and also the relative position between the license plate and camera at the passage of a toll station varies, the detected fingerprints of the same vehicle are usually slightly different, making the comparison difficult .
  • fingerprints include many features, i. are similar to high-dimensional vectors, which makes the search for similar fingerprints very compute-intensive and even with database techniques is not fully possible, especially if the databases contain very many entries, as is the case with video-based systems.
  • the invention has for its object to provide methods for automatic license plate recognition and toll categorization, which have a lower error rate and higher reliability than the known methods and thereby are quick and easy to implement.
  • the method of the invention is based on the use of OCR tag read results and fingerprints and is based on the recognition that the errors of OCR readings are not randomly distributed, but that the same misreads occur again and again for one and the same tag.
  • a license plate ABC123 is usually read correctly, but occasionally as ABCI23 or as A8C123.
  • the method of the invention proposes to store in the database a list of possible reading results of the automatic license plate recognition, which contains only those known variants of license plate reading results for the license plate of the vehicle, which meet minimum quality or quantity requirements.
  • n assignment list of possible license plate reading results and vehicle license plates is successively filled during the operation of the toll system. Newly created vehicle accounts have only entered the correct indicator in the list of possible license plate reading results. For each misreading of the indicator, the reading result is checked manually, assigned to a vehicle account and added to the list of possible license plate reading results. Each read variant of the license plate only needs to be processed once and entered into the vehicle account.
  • the invention according to claim 1 is accordingly characterized in that, if in step d) no identical label reading result is determined or in step e) no similar or no similar candidate fingerprint can be determined, the vehicle identifier for the current label reading result is determined manually.
  • the vehicle license plate in the database is assigned the current license plate reading result as a new or additional license plate reading result and preferably also the current fingerprint as a new or additional reference fingerprint if the current license plate reading result reaches a minimum frequency and / or minimum quality.
  • an association is also removed from the database if it is not used within a predetermined period of time.
  • step d) it is also useful to use the read variants in step d) in the search for associated vehicle accounts or fingerprints and / or when creating the assignment list as possible reading results of a license plate to save.
  • the fingerprint may be generated in any manner known in the art. It is particularly advantageous if the sensory detection takes place by means of a camera with which an image of the license plate and / or the vehicle is made, from which features are extracted and from which the fingerprint is calculated. Alternatively or additionally, the sensory detection can take place by means of sensors such as light barriers, laser scanners, induction loops, weight sensors or radio communication means with which characteristic features of the vehicle are detected, from which the fingerprint is calculated.
  • sensors such as light barriers, laser scanners, induction loops, weight sensors or radio communication means with which characteristic features of the vehicle are detected, from which the fingerprint is calculated.
  • a further preferred embodiment of the invention is accordingly distinguished by storing weightings for individual regions of the assigned reference fingerprints in the database for each license plate reading result, preferably for each individual assignment thereof to a vehicle license plate, which are taken into account in the comparison of similarity. These weights form the so-called "information about the relevant differences”.
  • toll systems there is often the problem that vehicles that are not subject to tolls are classified as toll.
  • An example is the classification of a toll-free large car (eg a van) as a toll vans.
  • This misclassification stems from measurement inaccuracies in the sensors for the classification or is simply due to the fact that some vehicle classes can not be defined on the basis of defined external characteristics.
  • the misclassification must be corrected manually. Often, the correction must be made on the same vehicle at each vehicle passage.
  • the inventive method can still be used profitably in these cases, if in the vehicle list an entry for the vehicle is stored in which is entered that the vehicle is not subject to toll.
  • a preferred embodiment of the invention is that the tag reading results are stored in encrypted form in the database and the current tag reading result for the equality search of step d) is encrypted in the same manner.
  • the method of the invention can preferably be used in addition to the toll categorization of the vehicle by also assigning a toll category to each reference fingerprint in the database and, if this reference fingerprint is determined to be the most similar candidate fingerprint, its assigned toll category.
  • Fig. 1 shows the method for automatic license plate recognition based on an image 1 of a vehicle 2 with a vehicle registration plate LP in the context of a road toll system.
  • vehicle includes any type of land, air and water vehicles, both self-propelled and externally propelled.
  • a first step (b) an OCR reading of the license plate LP of the vehicle 2 takes place from the image 1 in order to obtain a current license plate reading result LP OCR , eg "ABC123".
  • one or more characteristic features of the vehicle 2 are sensed in order to obtain therefrom a so-called "current fingerprint" FP of the vehicle 2.
  • characteristic features are, for example, the appearance of the License plate, the vehicle color, the appearance of the vehicle front, etc., which can serve to verify the vehicle identity, and / or the vehicle dimensions, the vehicle shape, the presence of a trailer, etc., which can be used for example for the toll.
  • the fingerprint FP can be extracted directly from the camera image 1, e.g. by determining significant bright / dark areas, calculating image parameters such as contrast, cross sums, etc., u.zw. both of the entire vehicle 2 and parts of the vehicle 2.
  • image parameters such as contrast, cross sums, etc., u.zw. both of the entire vehicle 2 and parts of the vehicle 2.
  • the fingerprint FP is formed in this way from the image of the vehicle registration plate LP itself.
  • the fingerprint FP can be obtained from other or further images 1 of the vehicle 2, from data from sensors such as laser scanners, light barriers, induction loops, video cameras, weight sensors, radio systems, e.g. RFID transponder queries, etc. are determined. Different types of sensor data can also be combined in one and the same fingerprint FP.
  • the license plate reading result LP OCR is looked up in a database DB of the road toll system.
  • the database DB contains a first list 3 with stored license plate reading results LP OCR, men and a second list 4 with all vehicle license plates LP registered in the road toll system and associated reference fingerprints FP ref .
  • the current mark reading result LP OCR can be looked up directly in the list 3 of the stored mark reading results LP OCR, mem , since there is only one identical record LP OCR, mem in the list 3 for each possible mark reading result LP OCR This makes the implementation significantly easier and faster.
  • step (e) all reference fingerprints FP ref of the relevant vehicle identifier LP that match the current license plate reading result LP OCR are then determined as "candidate fingerprints" FP ref, cand and respectively compared with the current fingerprint FP.
  • This comparison is a similarity comparison in which the "most similar" candidate fingerprint FP ref, cand is determined; the vehicle identifier LP assigned thereto is outputted as a result of the license plate recognition process (step f).
  • a wide variety of similarity criteria can be specified, for example the maximum value of a numerically calculated similarity measure and / or exceeding a minimum similarity measure etc.
  • it can also be checked whether the similarity criteria for no other Candidate fingerprint apply and thus the "most similar" candidate fingerprint is uniquely determined, ie only a clearly most similar candidate fingerprint is used for further processing.
  • LP reference fingerprints FP ref can be calculated from different image recordings showing the license plate of the vehicle 2 under different light conditions or viewing angles for one and the same identifier.
  • the similarity comparisons of the current fingerprint FP can be combined with all candidate fingerprints FP ref, cand one and the same identifier LP to a common similarity value, eg by adding up, by selecting the maximum value, etc.
  • step (d) If no similar label reading result LP OCR, mem in the list 3 or in step (e) no similar or no similar candidate fingerprint FP ref, can be determined in step (d), a branch is made in each case to a step (g) the license plate LP by an operator manually, in particular visually, determined from the image 1 and the current license plate reading result LP OCR and from this a new entry for the database DB is generated.
  • the manually determined vehicle identifier LP is assigned the current license plate reading result LP OCR as a new or additional license plate reading result LP OCR, mem and / or the current fingerprint FP as a new or additional reference fingerprint FP ref . It can also be checked whether the current license plate reading result LP OCR or the current fingerprint FP reaches a minimum frequency and / or a minimum quality that qualifies it or him to be included in the database DB.
  • Criteria are also preferably provided to remove assignments of license plate reading results LP OCR, mem to license plate LP from the list 3 of the database DB, for example if they are no longer being accessed, for example because a license plate has been renewed and thus become more readable is, so that certain license plate reading results no longer occur.
  • weightings WT for reference fingerprints FP ref can also be determined in a step (h) and stored in the database DB for each assignment LP OCR, mem to an identifier LP, which weightings WT are taken into account in the fingerprint comparison of step (e), around this to focus on particularly distinctive or relevant areas of the fingerprints FP and FP ref, cand to be compared.
  • step (d) Some OCR tag recognition methods provide several alternative tag reading results, one most likely to be most likely, while the other are possible but less likely alternatives.
  • the alternative label reading results may be used beneficially in step (d) and step (g).
  • step (d) the alternative read results can also be looked up in the list 3 of the stored label reading results LP OCR, mem and the associated reference fingerprints FP ref can be used for the fingerprint comparison.
  • step (g) all possible read variants of the flag can be entered in the database DB.
  • the label reading results LP OCR, mem stored in the list 3 can be encrypted for privacy reasons.
  • the current label reading result LP OCR is then encrypted in the same manner; it is therefore not necessary to use, transfer or store in the database DB or in the context of the present method license plate reading results in plain text.
  • the encryption can be done by any known method which ensures that label read results LP OCR , LP OCR, mem can not be accessed without authorization. In fact, in the case of the method presented here, it is not necessary at all to know or decrypt the license plate reading result in plain text, ie an irreversible encryption can be used.
  • each encryption result is only generated exactly by a license reading result:
  • the method is based on comparing a current license plate reading result with a list stored license plate reading result and looking for exact matches, not just similar characteristics .
  • Such a search for an exact match can also be carried out if both the current license plate reading result LP OCR and the stored license plate reading result LP OCR, mem are present in a form encrypted in the same way.
  • the encryption is a so-called "one-way function", i. a difficult invertible function, e.g. an asymmetric encryption with public and private key.
  • the private key that would be needed to decrypt can be destroyed so that no one can infer the actual label reading result from the encrypted label reading result.
  • Encrypting the license plate reading results also makes it possible to apply the method to section control, parking and other closed systems, which place particularly stringent data protection requirements. It is to distinguish between closed systems in which a vehicle passage is assigned at the exit from the closed system of a vehicle passage at the entrance to the closed system, and systems with registered users, in which vehicle passages are assigned to a vehicle account. For closed systems (without registration) saving a list of possible license plate reading results does not make sense. However, also in these systems, a preselection of fingerprints based on the license plate reading result, whereby the encryption of the license plate reading results may be required for data protection reasons, especially in these applications.
  • a very advantageous extension of the method described is to use the location and time of a vehicle passage in the recognition of the vehicles to make plausibility of the assignment of the vehicle passages to vehicle accounts. In doing so, e.g. Checked from several vehicle detections, whether a vehicle could be at a specific location at the specified time.
  • Fig. 2 shows a further advantageous embodiment of the invention, in addition to the automatic license plate recognition and an automatic toll categorization of a vehicle is performed, on the basis of the discussed comparison of fingerprints, the fingerprint may include features with which the vehicle characteristics can also be verified.
  • the reference fingerprint (s) FP ref of an identified vehicle having an identification ID, eg its vehicle identifier LP is also assigned one (or more) toll category (s) MK. If the current fingerprint FP of the vehicle passage with one of the above-identified candidate fingerprints FP ref, cand best similar resembles, the associated toll category MK is taken from the database DB and output in step (f) (see also Fig. 1 ).
  • a van passes through a toll station during a first vehicle passage.
  • the sensors installed at the toll station e.g. a laser scanner, detects the vehicle and classifies it (wrongly) as a pickup truck. As the van only pays for the (lower) toll category "Van", a contradiction is found.
  • the fingerprint of the current passage is compared with the reference fingerprint stored in the vehicle account. If the two fingerprints match (i.e., the similarity measure is above a predetermined threshold value), the toll category determined by the processor during the first passage is adopted and the contradiction is thus automatically corrected.
  • the method is based on the knowledge that one and the same vehicle, which has not changed in its outer shape, can be assigned to the same toll category again.
  • a first step the identification of the vehicle or the assignment to a vehicle account, e.g. with the aid of the method previously described for automatic license plate recognition.
  • a second step it is ensured by means of the fingerprint comparison that changes to the vehicle are detected and the toll category stored in the vehicle account is only accepted if the vehicle has remained externally unchanged. Examples of changes to the vehicle are when the vehicle is being renewed (e.g., same license plate on new vehicle) or e.g. carries a trailer or caravan.
  • a vehicle with a specific identification ID or LP can also have different reference fingerprints FP ref for its appearance be assigned in different configurations K with associated toll categories MK, so that one and the same vehicle in different configurations K, eg with and without trailers, can be automatically categorized and vermautet.
  • only the outer dimensions of the vehicle can be stored as reference fingerprint FP ref ;
  • the presence of a trailer can then be easily recognized eg by a large change in length.
  • Another very advantageous embodiment is to save trailers as their own vehicles in the database DB with its own reference fingerprint.
  • the towing vehicle and trailer are identified separately and optionally the characteristics of towing vehicle and trailer are verified.
  • the determined toll category MK then results from a combination of the categories of the towing vehicle and the trailer.
  • the determination of the toll category MK by means of fingerprints FP can be based on the presented here method for license plate recognition based on OCR license plate reading results LP OCR , in which case can be used as a vehicle identification ID directly its flag LP.
  • any other known method for vehicle identification could be used for this, for example a vehicle identification by means of an electronic tag attached to the vehicle, such as an RFID transponder chip or an onboard unit (OBU) for electronic toll debiting.

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  • Business, Economics & Management (AREA)
  • Finance (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Collating Specific Patterns (AREA)
  • Devices For Checking Fares Or Tickets At Control Points (AREA)
  • Traffic Control Systems (AREA)
EP09450175A 2009-09-18 2009-09-18 Verfahren zur Kennzeichenerkennung und Mautkategorisierung von Fahrzeugen Active EP2320384B1 (de)

Priority Applications (6)

Application Number Priority Date Filing Date Title
ES09450175T ES2401324T3 (es) 2009-09-18 2009-09-18 Procedimiento para el reconocimiento de matrícula y la categorización de peaje de vehículos
EP09450175A EP2320384B1 (de) 2009-09-18 2009-09-18 Verfahren zur Kennzeichenerkennung und Mautkategorisierung von Fahrzeugen
DK09450175.6T DK2320384T3 (da) 2009-09-18 2009-09-18 Fremgangsmåde til nummerpladeidentificering og afgiftskategorisering af køretøjer
PL09450175T PL2320384T3 (pl) 2009-09-18 2009-09-18 Sposób rozpoznawania numeru rejestracyjnego i kategoryzacji opłat drogowych
SI200930504T SI2320384T1 (sl) 2009-09-18 2009-09-18 Postopek prepoznavanja registrske tablice in cestninskega kategoriziranja vozil
PT94501756T PT2320384E (pt) 2009-09-18 2009-09-18 Processo para o reconhecimento de matrículas e atribuição de classe de portagem a veículos

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP09450175A EP2320384B1 (de) 2009-09-18 2009-09-18 Verfahren zur Kennzeichenerkennung und Mautkategorisierung von Fahrzeugen

Publications (2)

Publication Number Publication Date
EP2320384A1 EP2320384A1 (de) 2011-05-11
EP2320384B1 true EP2320384B1 (de) 2012-12-12

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EP09450175A Active EP2320384B1 (de) 2009-09-18 2009-09-18 Verfahren zur Kennzeichenerkennung und Mautkategorisierung von Fahrzeugen

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EP (1) EP2320384B1 (pt)
DK (1) DK2320384T3 (pt)
ES (1) ES2401324T3 (pt)
PL (1) PL2320384T3 (pt)
PT (1) PT2320384E (pt)
SI (1) SI2320384T1 (pt)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102011053052B3 (de) 2011-08-26 2013-02-28 Jenoptik Robot Gmbh Verfahren und Vorrichtung zur Identifikation von Kraftfahrzeugen zur Verkehrsüberwachung
CN104123844A (zh) * 2014-08-12 2014-10-29 陈昊 用于鉴别真假（套）车牌的信息处理模块
DE102014225804A1 (de) 2014-12-15 2016-06-16 Bayerische Motoren Werke Aktiengesellschaft Unterstützung beim Führen eines Fahrzeugs
WO2016164730A1 (en) 2015-04-09 2016-10-13 Veritoll, Llc License plate matching systems and methods
EP3869396A1 (de) 2020-02-24 2021-08-25 Toll Collect GmbH Verfahren und system zur korrektur eines kennzeichens
CN114694385B (zh) * 2020-12-25 2023-04-28 富联精密电子(天津)有限公司 停车管理方法、装置、系统、电子设备及存储介质

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SG10201508738UA (en) * 2005-06-10 2015-11-27 Accenture Global Services Ltd Electronic vehicle identification

Also Published As

Publication number Publication date
PL2320384T3 (pl) 2013-05-31
SI2320384T1 (sl) 2013-03-29
EP2320384A1 (de) 2011-05-11
DK2320384T3 (da) 2013-03-18
PT2320384E (pt) 2013-03-12
ES2401324T3 (es) 2013-04-18

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