WO2014110629A1 - Automated vehicle recognition - Google Patents
Automated vehicle recognition Download PDFInfo
- Publication number
- WO2014110629A1 WO2014110629A1 PCT/AU2014/000029 AU2014000029W WO2014110629A1 WO 2014110629 A1 WO2014110629 A1 WO 2014110629A1 AU 2014000029 W AU2014000029 W AU 2014000029W WO 2014110629 A1 WO2014110629 A1 WO 2014110629A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- image
- vehicle
- sub
- matching
- features
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
- G06T7/74—Determining position or orientation of objects or cameras using feature-based methods involving reference images or patches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
- G06V20/625—License plates
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/08—Detecting or categorising vehicles
Definitions
- the present invention relates to recognising vehicles, and in particular to a system and method configured to recognise a vehicle based on visible features of the vehicle including the registration plate and also including other visible features of the vehicle.
- One application of automated vehicle identification is in relation to electronic toll collection.
- Electronic toll collection is typically effected by equipping a user's vehicle with an electronic transponder.
- the transponder communicates with the toll booth and the applicable toll is deducted from the user's account.
- a camera is usually provided if a vehicle passes through without a transponder, so that an off-line payment or penalty fine can subsequently be obtained from the driver by tracking the vehicle registration plate.
- Vehicle identification can also be desirable in other applications such as street parking enforcement, parking centre enforcement, vehicle speed enforcement, point-to-point vehicle travel time measurements, and the like.
- the present invention provides a method of identifying a vehicle, the method comprising:
- the present invention provides a system for identifying a vehicle, the system comprising:
- At least one camera for obtaining at least one image of a vehicle
- the present invention provides a computing device configured to carry out the method of the first aspect.
- the present invention provides a computer program product comprising computer program code means to make a computer execute a procedure for identifying a vehicle, the computer program product comprising computer program code means for carrying out the method of the first aspect.
- the first and second sub-images preferably comprise two of: a vehicle license plate sub-image, a vehicle logo sub-image, and a vehicle region of interest sub-image. In some embodiments all three such sub-images may be extracted, matched and score fused.
- the sub- images preferably consist of wholly distinct sub-areas of the at least one obtained image.
- the region of interest may comprise one or more of: a vehicle fender, for example to match bumper stickers; or a particular vehicle panel, for example to match stained or dirty portions of the vehicle, a colour of the vehicle or damage to the vehicle.
- first and second sub-images may overlap partly or completely, and may for example both be images of a license plate of the vehicle.
- each set of image features comprises image features which are tolerant to image translation, scaling, and rotation, as may occur between images of the same vehicle taken at different times and/or in different locations. Additionally or alternatively, each set of image features preferably comprises image features which are tolerant to changes in illumination and/or low bit-rate storage for fast matching.
- extracting the first and/or second set of image features may comprise a first step of coarse localisation of feature key points in the respective sub-image. Localised feature key points preferably have a well-defined position in image space and have a local image structure which is rich in local information. For example, feature key points may be localised by a corner detection technique, or more preferably by combined used of multiple corner detection techniques.
- the first and/or second set of image features are vetted in order to eliminate unqualified feature points.
- one or more robust descriptors of each key point are preferably obtained.
- the descriptors are preferably robust in the sense of being somewhat invariant to changes in scaling, rotation, illumination and the like.
- matching the first set of image features to corresponding image features derived from a previously obtained image of a vehicle to produce a first matching score may comprise applying distance matching and voting techniques in order to determine the match between the descriptors of one feature key point of the first set of image features to the descriptors of a corresponding feature key point in the previously obtained image.
- geometric alignment is used to reduce the false matching of feature points.
- the vehicle may be imaged while passing a toll booth, at a parking location, in motion on a road or at other suitable location.
- Fusing may be performed in accordance with WO/2008/025092 by the same applicant as the present application, the contents of which are incorporated herein by reference.
- Identifying a character region (license plate) in an image may be performed in accordance with the teachings of WO/2009/052577 by the same applicant as the present application, the contents of which are incorporated herein by reference.
- Verification of identification of an image characteristic may be performed over multiple image frames in accordance with the teachings of WO/2009/052578 by the same applicant as the present application, the contents of which are incorporated herein by reference.
- Toll plaza throughput is a significant factor and detecting a license plate alone may not be possible in high throughput booths with high vehicle speeds.
- Embodiments of the present invention which rely only on one or more camera images, necessitate no additional infrastructure at the toll booth beyond a camera and the conventional transponder communication system.
- Some embodiments of the present invention thus recognise that, in addition to tolling a transponder borne by a vehicle, there is a need to recognise the vehicle itself in order to ensure that the correct tolling rate is being applied to that vehicle.
- Figure 1 is an overview schematic of a system in accordance with one embodiment of the present invention
- FIG. 2 illustrates the automated vehicle identification process implemented in the embodiment of Figure 1;
- FIG. 3 illustrates extraction of three sub-images in accordance with the embodiment of Figure 1;
- Figure 4 illustrates the feature extraction and matching process applied to each sub-image in the embodiment of Figure 1;
- FIGS 5a and 5b illustrate coarse localisation of key points, and key point qualification
- Figure 6 illustrates plate matching
- FIGS 7a to 7d illustrate box filters suitable for use in one embodiment of the invention. Description of the Preferred Embodiments
- FIG. 1 is an overview schematic of a system in accordance with one embodiment of the present invention.
- a vehicle 102 is imaged by a camera 110, for example when passing a tolling site. Images from camera 110 are passed to a vehicle matching system 120, which includes an image processor 122 and a database 124 containing obtained vehicles images and/or image feature descriptors.
- Figure 2 illustrates the automated vehicle identification process implemented in the embodiment of Figure 1.
- This embodiment uses an image processing technique for identifying distinguishing features of vehicles and thereby identifying and recognizing vehicles based on captured images. This technique utilizes visual characteristics of a vehicle to extract unique image feature descriptors which uniquely identify each imaged vehicle.
- unique image feature descriptors for each physical vehicle are extracted from each captured image. This can be considered as a vector of feature values which uniquely represents each vehicle.
- a license plate sub-image, logo sub-image and region of interest (ROI) sub-image are extracted from the captured image (see FIG 3, and STEP 1 in Figure 2). Identifying a character region (license plate) in an image may be performed in accordance with the teachings of WO/2009/052577.
- a region of interest (ROI) sub-image is manually defined from the captured image based on the view of camera.
- the method consists of three main steps as illustrated in FIG 4, namely feature key point detection, feature descriptor derivation, and feature matching.
- the feature key-point detection step consists of two steps: coarse localization of feature key-points; and elimination of unstable key-points.
- interest points are detected in the license plate image.
- the feature key-points or interest points should have a well-defined position in image space and the local image structure around them should be rich in terms of local information.
- the present embodiment identifies interest points in the license plate image using the technique described in SURF (Speeded Up Robust Feature (SURF) (Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346-359, 2008)).
- SURF Speeded Up Robust Feature
- Suitable corner detection techniques include Moravec corner detection (H. Moravec (1980). "Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover”. Tech Report CMU-RI-TR-3 Carnegie-Mellon University, Robotics Institute), Harris and Stephens corner detection (C. Harris and M. Stephens (1988). "A combined corner and edge detector”.
- the Harris detector for example is rotation-invariant, so that even if the image is rotated, it can find the same corners, a problem is that when the image is scaled a corner may not be detected as a corner anymore.
- the present embodiment identifies interest points in the license plate image using the technique described in SURF. D.Lowe, University of British Columbia, proposed a new algorithm, Scale Invariant Feature Transform (SIFT) in his paper, "Distinctive Image Features from Scale-Invariant Keypoints" by using scale- space extrema detection to find key-points.
- SIFT Scale Invariant Feature Transform
- the Laplacian of Gaussian (LoG) is found for the image with various ⁇ values ( ⁇ acts as a scaling parameter).
- SIFT uses Difference of Gaussians (DoG) as an approximation of LoG. This process is done for different octaves of the image in Gaussian Pyramid.
- DoG Difference of Gaussians
- the preferred SURF approach is advantageous because SURF approximates the LoG with box filters. These box filters (shown in Figures 7c and 7d) are used to approximate second order Gaussian derivatives and can be evaluated at a very low computational cost using integral images. SURF also relies on determinant of Hessian matrix for both scale and location.
- One advantage of this approximation is that convolution with such a box filter can be easily calculated with the help of integral images, and moreover can be performed in parallel for different scales.
- each octave is composed of 4 box filters, which are defined by the number of pixels on their side (denoted by s).
- NMS Non-Maximum Suppression
- FIG. 6 illustrates some examples of matching plates.
- Feature descriptor extraction is then applied to the qualified key points. For each key- point identified in the previous step, the process seeks to extract local feature information around that key-point, and specifically information which is reasonably invariant to illumination changes, to scaling, rotation and minor changes in viewing direction.
- feature descriptors include: Scale-invariant feature transform (SIFT) (D. Lowe (2004). “Distinctive Image Features from Scale-Invariant Keypoints”.
- This feature descriptor uses Wavelet responses in horizontal and vertical direction (whereby the use of integral images advantageously eases computational load and scale tolerance).
- a neighbourhood of size 20sX20s is taken around the key-point where s is the size. It is divided into 4x4 subregions. For each subregion, horizontal and vertical wavelet responses are taken and a vector is formed
- v SURF feature descriptor is represented as a 64-dimension vector.
- HOG Histogram of Oriented Gradients
- each key-point four feature descriptor vectors are found using the above four techniques respectively.
- the uniquely identifying information for each vehicle, stored in database 124 then comprises all key-point locations and a set of four local feature descriptors for each key -point.
- Feature matching follows. Each type of local feature is matched separately. Distance matching and voting algorithms are used to determine the match of a pair of feature points from two corresponding plates. For distance matching, a distance measure is defined between two feature vectors as the Euclidian distance. For voting, the distance to the best matching feature is compared to the distance to the second best matching feature. If the ratio of closest distance to second closest distance is greater than a predefined threshold (0.85 in this embodiment) then the match is rejected as a false match.
- a matching score is calculated which in this embodiment is simply equal to the number of matching points.
- Step 2 of Figure 2 the same feature extraction and matching algorithm are also applied on the logo sub-image and the ROI sub-image.
- a threshold is used to make a decision of whether there is a match, or not.
- the threshold is set experimentally, based on the receiver operating characteristic (ROC).
- ROC receiver operating characteristic
- the true matching rate (TMR) is the ratio of an existing (in database) car being matched.
- the false matching rate (FMR) is the ratio of a non-existing (in database) car being matched.
- the true rejecting rate (TRR) is the ratio of a non-existing (in database) car being rejected.
- the false rejecting rate (FRR) is the ratio of an existing (in database) car being rejected.
- a two stage approach to vehicle matching may be adopted, wherein conventional optical character recognition (OCR) of license plates may be applied as a first stage. If an OCR match is found in this first stage, the vehicle match is confirmed. If an OCR match is not found in the first stage, the above-described embodiment is applied as a second stage.
- a soft-decision classifier based on simple probabilistic classifier, the Bayes classifier.
- the probability model for the classifier is a conditional model, and can be written as: F 2 ,..., F n ), where C is the dependent class variable of matching vehicles, and F l , F 2 ,..., F n axe feature variables.
- the feature variables are the OCR matching score (based on Levenshtein distance) and vehicle DNA matching scores, over single or multiple image frames.
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Image Analysis (AREA)
- Traffic Control Systems (AREA)
Abstract
Description
Claims
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/761,937 US20150371109A1 (en) | 2013-01-17 | 2014-01-17 | Automated vehicle recognition |
EP14740877.7A EP2946340A4 (en) | 2013-01-17 | 2014-01-17 | Automated vehicle recognition |
AU2014207250A AU2014207250A1 (en) | 2013-01-17 | 2014-01-17 | Automated vehicle recognition |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU2013900153A AU2013900153A0 (en) | 2013-01-17 | Automated Vehicle Recognition | |
AU2013900153 | 2013-01-17 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2014110629A1 true WO2014110629A1 (en) | 2014-07-24 |
Family
ID=51208871
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/AU2014/000029 WO2014110629A1 (en) | 2013-01-17 | 2014-01-17 | Automated vehicle recognition |
Country Status (4)
Country | Link |
---|---|
US (1) | US20150371109A1 (en) |
EP (1) | EP2946340A4 (en) |
AU (1) | AU2014207250A1 (en) |
WO (1) | WO2014110629A1 (en) |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
FR3034557A1 (en) * | 2015-04-03 | 2016-10-07 | Tingen Tech Co Ltd | METHOD AND SYSTEM FOR AUTOMATIC PLANNING OF MAINTENANCE OF VEHICLES |
CN109727188A (en) * | 2017-10-31 | 2019-05-07 | 比亚迪股份有限公司 | Image processing method and its device, safe driving method and its device |
CN110659688A (en) * | 2019-09-24 | 2020-01-07 | 江西慧识智能科技有限公司 | Monitoring video riot and terrorist behavior identification method based on machine learning |
CN111178291A (en) * | 2019-12-31 | 2020-05-19 | 北京筑梦园科技有限公司 | Parking payment system and parking payment method |
US10867193B1 (en) | 2019-07-10 | 2020-12-15 | Gatekeeper Security, Inc. | Imaging systems for facial detection, license plate reading, vehicle overview and vehicle make, model, and color detection |
US20220300749A1 (en) * | 2021-03-19 | 2022-09-22 | Apple Inc. | Configurable keypoint descriptor generation |
US11538257B2 (en) | 2017-12-08 | 2022-12-27 | Gatekeeper Inc. | Detection, counting and identification of occupants in vehicles |
US11736663B2 (en) | 2019-10-25 | 2023-08-22 | Gatekeeper Inc. | Image artifact mitigation in scanners for entry control systems |
Families Citing this family (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9550120B2 (en) * | 2014-12-08 | 2017-01-24 | Cubic Corporation | Toll image review gamification |
JP2017204025A (en) * | 2016-05-09 | 2017-11-16 | 株式会社駐車場綜合研究所 | Server device and program |
CN106529424B (en) * | 2016-10-20 | 2019-01-04 | 中山大学 | A kind of logo detection recognition method and system based on selective search algorithm |
CN106778777B (en) * | 2016-11-30 | 2021-07-06 | 成都通甲优博科技有限责任公司 | Vehicle matching method and system |
CN110889867B (en) * | 2018-09-10 | 2022-11-04 | 浙江宇视科技有限公司 | Method and device for detecting damaged degree of car face |
CN109508731A (en) * | 2018-10-09 | 2019-03-22 | 中山大学 | A kind of vehicle based on fusion feature recognition methods, system and device again |
KR101986592B1 (en) * | 2019-04-22 | 2019-06-10 | 주식회사 펜타게이트 | Recognition method of license plate number using anchor box and cnn and apparatus using thereof |
GB2600312B (en) * | 2019-07-19 | 2023-06-14 | Mitsubishi Heavy Ind Mach Systems Ltd | Number plate information specifying device, billing system, number plate information specifying method, and program |
US11829128B2 (en) | 2019-10-23 | 2023-11-28 | GM Global Technology Operations LLC | Perception system diagnosis using predicted sensor data and perception results |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040167861A1 (en) * | 2003-02-21 | 2004-08-26 | Hedley Jay E. | Electronic toll management |
US20060030985A1 (en) * | 2003-10-24 | 2006-02-09 | Active Recognition Technologies Inc., | Vehicle recognition using multiple metrics |
JP5057184B2 (en) * | 2010-03-31 | 2012-10-24 | アイシン・エィ・ダブリュ株式会社 | Image processing system and vehicle control system |
WO2012017436A1 (en) * | 2010-08-05 | 2012-02-09 | Hi-Tech Solutions Ltd. | Method and system for collecting information relating to identity parameters of a vehicle |
US8533204B2 (en) * | 2011-09-02 | 2013-09-10 | Xerox Corporation | Text-based searching of image data |
-
2014
- 2014-01-17 WO PCT/AU2014/000029 patent/WO2014110629A1/en active Application Filing
- 2014-01-17 EP EP14740877.7A patent/EP2946340A4/en not_active Withdrawn
- 2014-01-17 AU AU2014207250A patent/AU2014207250A1/en not_active Abandoned
- 2014-01-17 US US14/761,937 patent/US20150371109A1/en not_active Abandoned
Non-Patent Citations (3)
Title |
---|
CLADY, X. ET AL.: "Multi-class Vehicle Type Recognition System", PROCEEDINGS OF THE 3RD IAPR WORKSHOP ON ARTIFICIAL NEURAL NETWORKS IN PATTERN RECOGNITION (ANNPR '08), 2 July 2008 (2008-07-02), pages 228 - 239, XP019091885 * |
KUMAR, T. S. ET AL.: "Object Detection and Tracking in Video using particle filter", 2012 THIRD INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION & NETWORKING TECHNOLOGIES (ICCCNT, 26 July 2012 (2012-07-26), COIMBATORE, INDIA, pages 1 - 10, XP032442881 * |
See also references of EP2946340A4 * |
Cited By (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
FR3034557A1 (en) * | 2015-04-03 | 2016-10-07 | Tingen Tech Co Ltd | METHOD AND SYSTEM FOR AUTOMATIC PLANNING OF MAINTENANCE OF VEHICLES |
CN109727188A (en) * | 2017-10-31 | 2019-05-07 | 比亚迪股份有限公司 | Image processing method and its device, safe driving method and its device |
US11538257B2 (en) | 2017-12-08 | 2022-12-27 | Gatekeeper Inc. | Detection, counting and identification of occupants in vehicles |
US10867193B1 (en) | 2019-07-10 | 2020-12-15 | Gatekeeper Security, Inc. | Imaging systems for facial detection, license plate reading, vehicle overview and vehicle make, model, and color detection |
US11501541B2 (en) | 2019-07-10 | 2022-11-15 | Gatekeeper Inc. | Imaging systems for facial detection, license plate reading, vehicle overview and vehicle make, model and color detection |
CN110659688A (en) * | 2019-09-24 | 2020-01-07 | 江西慧识智能科技有限公司 | Monitoring video riot and terrorist behavior identification method based on machine learning |
US11736663B2 (en) | 2019-10-25 | 2023-08-22 | Gatekeeper Inc. | Image artifact mitigation in scanners for entry control systems |
CN111178291A (en) * | 2019-12-31 | 2020-05-19 | 北京筑梦园科技有限公司 | Parking payment system and parking payment method |
CN111178291B (en) * | 2019-12-31 | 2021-01-12 | 北京筑梦园科技有限公司 | Parking payment system and parking payment method |
US20220300749A1 (en) * | 2021-03-19 | 2022-09-22 | Apple Inc. | Configurable keypoint descriptor generation |
US11475240B2 (en) * | 2021-03-19 | 2022-10-18 | Apple Inc. | Configurable keypoint descriptor generation |
Also Published As
Publication number | Publication date |
---|---|
US20150371109A1 (en) | 2015-12-24 |
EP2946340A1 (en) | 2015-11-25 |
EP2946340A4 (en) | 2016-09-07 |
AU2014207250A1 (en) | 2015-08-20 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20150371109A1 (en) | Automated vehicle recognition | |
Sochor et al. | Boxcars: 3d boxes as cnn input for improved fine-grained vehicle recognition | |
Wang et al. | Improved human detection and classification in thermal images | |
Polishetty et al. | A next-generation secure cloud-based deep learning license plate recognition for smart cities | |
Abedin et al. | License plate recognition system based on contour properties and deep learning model | |
Wu et al. | A practical system for road marking detection and recognition | |
Puranic et al. | Vehicle number plate recognition system: a literature review and implementation using template matching | |
Karwal et al. | Vehicle number plate detection system for indian vehicles | |
Hu et al. | Video object matching based on SIFT algorithm | |
Zakir et al. | Road sign detection and recognition by using local energy based shape histogram (LESH) | |
Prates et al. | Brazilian license plate detection using histogram of oriented gradients and sliding windows | |
CN103218621A (en) | Identification method of multi-scale vehicles in outdoor video surveillance | |
Iqbal et al. | Image based vehicle type identification | |
Ng et al. | Detection and recognition of malaysian special license plate based on sift features | |
Hota et al. | On-road vehicle detection by cascaded classifiers | |
Yilmaz | A smart hybrid license plate recognition system based on image processing using neural network and image correlation | |
da Silva et al. | ALPRs-A new approach for license plate recognition using the SIFT algorithm | |
Farajzadeh et al. | Vehicle logo recognition using image matching and textural features | |
Ilayarajaa et al. | Text recognition in moving vehicles using deep learning neural networks | |
KR101733288B1 (en) | Object Detecter Generation Method Using Direction Information, Object Detection Method and Apparatus using the same | |
Zhu et al. | Car detection based on multi-cues integration | |
Deb et al. | Automatic vehicle identification by plate recognition for intelligent transportation system applications | |
Cosma et al. | Part-based pedestrian detection using HoG features and vertical symmetry | |
Das et al. | Bag of feature approach for vehicle classification in heterogeneous traffic | |
Syed et al. | Color edge enhancement based fuzzy segmentation of license plates |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 14740877 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
WWE | Wipo information: entry into national phase |
Ref document number: 14761937 Country of ref document: US |
|
REEP | Request for entry into the european phase |
Ref document number: 2014740877 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2014740877 Country of ref document: EP |
|
ENP | Entry into the national phase |
Ref document number: 2014207250 Country of ref document: AU Date of ref document: 20140117 Kind code of ref document: A |