Volume No-4, Issue No-1, January, 2016 Vehicle Identification and Tracking System based on Shape Parameters Analysis Kamaljit Kaur 1 Dr Rinkesh Mittal 2 Department of Electronics and Communication Engg, Department of Electronics and Communication Engg, CGCCOE Landran, Punjab, INDIA 12 CGCCOE Landran, Punjab, INDIA 12 Littlestar535@gmailcom Abstract-In the existing work, it is observed that the algorithms are language dependent ie characters and numbers should be in local language and if the language is changed, then the character identification algorithm will have to be changed each time the language changes This kind of problem has been targeted in the proposed work by using the statistical features of the text and can be fine-tuned easily just by replacing the statistical features data base of the particular language characters Though, it is observed that the vehicle is not only recognized by its number plate but the model of the motor vehicle also possesses many information so that it could be identified from the distance as the number plate can only be extracted once the camera is focussed on it But at the first sight, the vehicle is identified by it manifestation ie its shape, model, colour or style of the body Therefore, in order to identify a vehicle on road, prima facie, its data in terms of its body or appearance is an important source of information that must be retrieved or extracted from its image Keywords- Histogram of oriented gradients, Automatic Number Plate Recognition, binarizing I INTRODUCTION In the transport and traffic managing system, tracking or supervision of vehicles on road is very important Vehicles are identified with number plate and then from the record of the number plate contents the information is retrieve This way the system becomes complex and time consuming too The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection HOG counts occurrences in localized portions of an image Edge orientation histograms is similar to that of hog, but differs in that it is computed on a dense network of uniformly spaced cells and overlapping local contrast normalization is used to improve accuracy The vital role behind the histogram of oriented gradients descriptor is that local object appearance and shape within an image can be described by the distribution of intensity gradients or edge directions The image is segmented into small regions called cells, and for the pixels within each cell, a histogram of gradient directions is compiled For improved accuracy, the local histograms can be contrast-normalized by calculating a measure of the intensity across a larger region of the image, called a block, and then using this value to normalize all cells within the block This normalization results in better invariance to changes in illumination and shadowing The HOG descriptor has a few type of advantages over other descriptors Since it operates on local cells, it is invariant to arithmetic and photometric transformations Such changes would only appear in larger spatial regions The HOG descriptor is thus particularly suited for human detection in images II RELATED WORKS The paper introduces a machine learning system based on Artificial Neural Network for vehicle identification Haar filter is used for feature recognition of vehicles This system is merged with a Kalman filter, for multiple tracking of vehicles and thus damaged noisy signals are removed to construct an comprehensive vehicle identification and assessed on public province based images of vehicles on fluctuating traffic, clarification and climatic conditions Select samples and informative samples are retrained which provides high recalling rate, and give better localization effects on the recognized and trailed images This paper can be extended in the use of automated traffic control based on density of vehicles per unit time rather than fixed timings [1] This paper presents a recent template matching methods for detection and tracking of Vehicle Our focus is on systems where the camera is mounted on the vehicle and being fixed such as in traffic monitoring systems We discuss the general problem of on-road vehicle detection using template matching Also, discuss vehicle recognition through number plate and ways [2] In many countries around the world, Surveillance Cameras are used in enhancing the public security A traditional method in which stand alone cameras provide live feed and are still deployed at most of the places but the requirement of today s world is to add some intelligence to this systems Background Subtraction, segmentation, tracking and human behavior analysis are implemented to build such a system Lastly we talk about the future work which can lead to human behavior analysis like crowd counting, intrusion detection, loitering, crowd movement, vehicle tracking and all [3] A SVM classifier is used to extract the vehicle data for identification To acquire the vehicle image, histogram of gradients is used Further, the HoG are normalized with respect to size of the image/vehicle so that the HoG are not changed if the same image is giving zoom in or out effect Further, the HoG should not vary even if the image intensity is varied within the tolerance limits [4] This article presents an application of computer vision methods to traffic flow monitoring and road traffic analysis which utilizes image-processing and pattern recognition methods designed and constrains of road traffic analysis These methods combined together gives efficient capabilities of the system to monitor the road,, to measure the speed, to begin automated vehicle tracking, and also number plates of the car RES Publication 2012 Page 17 wwwijmeceorg
Volume No-4, Issue No-1, January, 2016 are recognized Developed Software was applied with video monitoring system, based on ordinary CCTV cameras connected to wide area network computers [5] In traffic, it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast which is the major problem in every country Therefore, it is not possible to grab the vehicle number from the moving vehicle because of the speed of the vehicle Therefore, Solutions to this problem is to develop Automatic Number Plate Recognition (ANPR) system Different methodologies are used but still there is a problem in some of the factors like high speed of vehicle, non-uniform vehicle number plate, characters of number plate and different lighting conditions can affect a lot in the overall detection rate In this paper, with parameters ie image size, success rate and processing time, different approaches of ANPR are discussed Towards the end of this paper, an addition to ANPR is suggested [6] This work is presented a real-time on-road vehicle tracking method which utilizes both shape and color information The follower builds statistical models of vehicle appearance for the target in color and shape feature spaces and continuously evaluates each of the feature spaces by using mean-shift algorithm The final position of the target is determined which are based on the similarity scores by fusing the possible locations found in different feature spaces together The planned method has been evaluated on real data, illustrating good performance [7] This paper developing a feature-based tracking system for detecting vehicles Instead of tracking entire vehicles, vehicle features are tracked to make the system strong to the problems of partial occlusion The system is fully functional under changing lighting conditions because the most important features at the given moment are tracked This paper describes the issues linked with feature based tracking, presents the realtime implementation of a sample system, and the performance of the system on a large data set[8] Automatic Vehicle Identification (AVI) has many applications in traffic systems (main road toll collection, red light disobedience enforcement, border and customs checkpoints, etc) License Plate Recognition is an effective form of AVI systems In this study, a smart and simple algorithm is presented for vehicle s license plate recognition system [9] This paper is presented a machine-vision system to detect vehicles running on red light or not follow the traffic rules The system operates during hours of daylight by getting video streams from two different sources One of them is a camera viewing the crossroads to identify unusual activity, while a second camera watches the semaphore to keep synchrony with the traffic controller During extended periods of time the system presentation and reliability have been tested on a real vehicular crossroads [10] Vehicle detection and tracking applications play a significant role in highway traffic surveillance control and urban traffic development In this paper, we present a brief overview of image processing methods and analysis tools which are used for vehicle tracking, counts, standard speed of each individual vehicle, traffic analysis that involved in rising traffic surveillance systems [11] The paper presents an approach for detecting vehicles in city traffic scenes by means of rule-based reasoning on visual data The strength of this is its formal separation between the lowlevel (used for extracting visual data under various lighting conditions) and the high-level module, which provides a general-purpose knowledge-based framework for tracking vehicles in the scene The synergy between the artificial intelligence techniques of the high-level and the low-level image analysis techniques provides the system with flexibility and robustness [12] License plate recognition (LPR) has greater efficiency for vehicle monitoring in automatic zone access control This Plate recognition system will keep away from special tags, since all vehicles have a unique registration number plate A number of techniques have been used for car plate characters recognition This system uses neural network character recognition and pattern matching of characters as two character recognition techniques In this approach multilayer feed- forward backpropagation algorithm is used The performance of the proposed algorithm has been tested on several car plates and provides very satisfactory results[13] This paper describes how to extract the vehicle license-plate and understanding of the registration code from a captured image in real-time The developed algorithm is divided into three stages: extraction of vehicle license plate from captured image, segmentation of license plate components and recognition of the license plate characters for from the registration code A control algorithm is also developed to choose between the recognition processes and rebuild the registration code from recognized characters [14] III ALGORITHM The objective of the proposed thesis work is to extract the vehicle information on road based on its appearance that is color, shape, model, any identifiable mark or any other source of information that is apparently available on the vehicle For, following objectives are summarised below: Image Acquisition of vehicles on road Segmentation of vehicle among different vehicles Image Enhancement and binarization Extraction of vehicle contour, size and other dimensional features Normalization of features with respect to zooming effect Vehicle data storage for its identification 31 Steps in Proposed Algorithm Step 1: Select Input of the original (RGB) image from Computer memory Step 2: RGB to Gray level conversion Step 3: Image Enhancement and binarization Step 4: Segmentation of texts and Hopfield Neural Network Training Step 5: Pixel Pattern for Hopfield NN for character Identification Step 6: Identification of Texts and store in a file Step 7: Complete no Plate identification and comparing with the data base to get the owner s information Segmentation RES Publication 2012 Page 18 wwwijmeceorg
Volume No-4, Issue No-1, January, 2016 The segmentation of vehicle in single frames is done using the pixel neighbourhood technique based on cluster labelling The cluster labelling is done using the 8-pixel neighbourhood connectivity The neighbourhood pixel connected, are labelled according to the loop count and the entire image is clustered and labelled and different labels are displayed in different image frames The objects of interest are segmented using the pixel neighborhood based segmentation This is done by labeling each clusters based on 8-pixel connectivity The biggest labeled cluster is taken out for analysis (x-1, y+1) (x, y-1) (x+1, y-1) (x-1, y) P (x,y) (x+1, y) (x-1, y- 1) (x, y+1) (x+1, y+1) Flow Chart Start Acquisition Image Acquisition Image Enhancement Image Segmentation Image Binarization Contour Extraction Dimensional; Analysis Normalization of Features Storage of vehicle features Vehicle Identification Results and Discussion End Acquisition RES Publication 2012 Page 19 wwwijmeceorg
Volume No-4, Issue No-1, January, 2016 Category Processing SNo Image No Area Perimeter radius R G B HOG time 1 I1 (Fig 51) accord 034 2068 0329 219 219 221 50911 8542 2 I2 (Fig 52) audi 048 2455 0391 118 117 133 51466 2009 Honda 3 I3 (Fig 53) city 049 2482 0395 219 230 236 49799 2156 4 I4 (Fig 54) alto 058 27 043 57 80 160 4831 1922 Maruti 5 I5 (Fig 55) 800 0513 2539 0404 130 178 224 5057 2205 6 I6 (Fig 56) nano 0293 1918 0305 255 226 181 49728 1938 Swift 7 I7 (Fig 57) dezire 0547 2623 0417 127 140 147 47814 2077 8 I8 (Fig 58) Swift vdi 0703 2971 0473 231 233 219 51153 2038 9 I9 (Fig 59) verna 0699 2964 0472 161 175 176 50884 1646 10 I10 (Fig 510) fortuner 0349 2094 0333 41 77 124 48679 2598 11 I11 (Fig 511) innova 0438 2346 0373 149 169 182 49798 235 12 I12 (Fig 512) Range rover 0294 1921 0306 241 237 245 50074 1905 13 I13 (Fig 513) safari 04 2243 0357 169 175 182 49578 163 14 I14 (Fig 514) scorpio 0548 2625 0418 70 74 79 5058 2858 15 I15 (Fig 515) thar 0561 2655 0423 119 119 130 49679 301 16 I16 (Fig 516) Ctu bus 015 1372 0218 190 200 200 50585 205 17 I17 (Fig 517) mercedes 0365 2142 0341 233 233 235 50208 191 18 I18 (Fig 518) Prtc bus 0027 0578 0092 176 161 138 49563 220 truck 19 I19 (Fig 519) 0429 2321 0369 239 242 243 49712 2078 20 I20 (Fig 520) truck 0351 2101 0334 0 5 2 50538 237 21 I21 (Fig 521) truck 015 1372 0218 139 81 95 49016 263 22 I22 (Fig 522) Volvo bus 0249 1769 0282 86 138 186 49747 2305 23 I23 (Fig 523) tractor 0322 2012 032 173 173 166 49477 248 24 I24 (Fig 524) tractor 0208 1617 0257 117 106 105 49514 2505 25 I25 (Fig 525) tractor 0169 1459 0232 222 232 231 4986 2714 26 I26 (Fig 526) auto 056 2653 0422 179 109 143 48625 295 27 I27 (Fig 527) auto 0286 1895 0302 72 71 82 49687 244 28 I28 (Fig 528) auto 0425 231 0368 163 176 179 52503 292 29 I29 (Fig 529) auto 069 2945 0469 133 154 154 51182 255 auto 30 I30 (Fig 530) 0111 1181 0188 210 200 194 50301 2676 31 I31 (Fig 531) bullet 0578 2695 0429 131 118 72 49859 270 32 I32 (Fig 532) bike 0564 2661 0424 146 41 48 5085 268 33 I33 (Fig 533) scooty 0658 2875 0458 236 226 232 48418 2336 34 I34 (Fig 534) scooter 0667 2896 0461 66 71 103 50839 215 Hero 329 35 I35 (Fig 535) honda 0379 2182 0347 18 16 20 52218 RES Publication 2012 Page 20 wwwijmeceorg
Volume No-4, Issue No-1, January, 2016 IV RESULTS registration number and thereby the owner information This will improve the vehicle tracking accuracy to great extent REFERENCES [1] B Karunamoorthy, Anupama J Nair, An Automated ANN Based Intelligent Syste For Vehicle Identification and Trail, pp no704-707, ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 2, February 2014 [2] Rajiv Kumar Nath, Dr Swapan Kumar Deb, On Road Vehicle/Objec Detection And Tracking Using Template, pp no98-107, ISSN : 0976-5166, Indian Journal of Computer Science and Engineering Vol 1 No 2, 98-107 [3] Niraj B Gadhe, Prof: Dr B K Lande, Prof: Dr BBMeshram, Intelligent System for detecting, Modeling, Classification of human behavior using image processing, machine vision and Open CV, pp no590-599, ISSN: 2278 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 [4] Dipankar Mudoi and Parismita A Kashyap, Vision Based Data Extraction of Vehicles in Traffic, pp no202-208, International Conference on Signal Processing and Integrated Networks (SPIN) 2014,IEEE V CONCLUSION The novelty lies in the fact that the image enhancement technique used in the system enables the faithful enhancement of the texts from the background without loss of any textual information For, we propose to use a maximum entropy algorithm for binarizing the input image This way we get a clearly segmented image between foreground and the background The presented work finds application in many aspects of traffic management system, like registration and renewal etc Further, the timing accuracy may be improved as the data base size increases In the presented work, a sample data base is generated However, if the data base size increases, the time consumption may be more Some work needs to be done in that area Further, the information in data base may be enriched with more vehicle information The algorithm designed here may be used for different language characters if trained for the input neurons of the same Further, the same may be used for different road signs and then transforming into the text pertaining to the road signs Other application may be in the field of text based application where auto conversion of image to text is required The proposed work may be embedded into a small miniaturized system in order to design a smart camera that can capture the image and give the results in print form to help the traffic personals The accuracy in vehicle tracking may be improved if the image acquisition device is so adjusted so that the number plate may be grabbed in the image This way, the number plate may be processed to get the vehicle [5] E Atkoˇci unas1, R Blake2, A Juozapaviˇcius1, M Kazimianec1, Image Processing in Road Traffic Analysis, Nonlinear Analysis: Modelling and Control, 2005, Vol 10, No 4, 315 332 [6] Chirag Patel, Dipti Shah, Atul Patel, Automatic Number Plate Recognition System (ANPR), pp no21-33, International Journal of Computer Applications (0975 8887) Volume 69 No9, May 2013 [7] Kai She, George Bebis, Haisong Gu, and Ronald Miller, Vehicle Tracking Using On-Line Fusion of Color and Shape Features [8] Benjamin Coif man, David Beyer, Philip MacLauchlan, Jitendra Malik, A real-time computer vision system for vehicle tracking and traffic surveillance, pp no 271-288, B Coifman et al/transportation Research Part C 6 (1998) [9] Serkan Ozbay, and Ergun Ercelebi, Automatic Vehicle Identification by Plate Recognition, World Academy of Science, Engineering and Technology 9 2005 [10] Sandra Luz Canchola Magdaleno, Joaquín Salas Rodríguez, Hugo Jiménez Hernández, A Machine-Vision System to Detect Unusual Activities Online at Vehicular Intersections, pp no 209-220, Computación y Sistemas Vol 13 No 2, 2009, ISSN 1405-5546) [11] Raad Ahmed Hadi, Ghazali Sulong and Loay Edwar George, Vehicle Detection And Tracking Techniques: A Concise Review, pp no 1-12, Signal & Image Processing : An International Journal (SIPIJ) Vol5, No1, February 2014 [12] Rita Cucchiara, Member, IEEE, Massimo Piccardi, Member, IEEE, and Paola Mello, Image Analysis and Rule-Based Reasoning RES Publication 2012 Page 21 wwwijmeceorg
Volume No-4, Issue No-1, January, 2016 for a Traffic Monitoring System, Ieee Transactions On Intelligent Transportation Systems, Vol 1, No 2, June 2000, pp 119-130 [13] Meenakshi1,R B Dubey2, Vehicle License Plate Recognition System,International Journal of Advanced Computer Research (ISSN (print): 227277 ISSN (online): 2277-7970) Volume-2 Number-4 Issue-7 December-2012 [14] Nahian Alam Siddique1, Asif Iqbal2, Fahim Mahmud, Md Saifur Rahman, Development of an Automatic Vehicle License Plate Detection and Recognition System for Bangladesh,pp no 688-693, IEEE/OSA/IAPR International Conference on Informatics, Electronics & Vision 978-1-4673-1154-0/12/$3100 2012 IEEE Author Profile Dr Rinkesh Mittal (HOD) of ECE from CGC-COE, Landran, Punjab, INDIA 1 Ms Kamaljit Kaur is pursuing her MTech in ECE from CGC-COE, Landran, Punjab, INDIA Her field of interest is in digital image processing based system development and integration RES Publication 2012 Page 22 wwwijmeceorg