Localised contourlet features in vehicle make and model recognition

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance

Using Prior Knowledge for Verification and Elimination of Stationary and Variable Objects in Real-time Images

With the evolving technologies in the autonomous vehicle industry, now it has become possible for automobile passengers to sit relaxed instead of driving the car. Technologies like object detection, object identification, and image segmentation have enabled an autonomous car to identify and detect an object on the road in order to drive safely. While an autonomous car drives by itself on the road, the types of objects surrounding the car can be dynamic (e.g., cars and pedestrians), stationary (e.g., buildings and benches), and variable (e.g., trees) depending on if the location or shape of an object changes or not. Different from the existing image-based approaches to detect and recognize objects in the scene, in this research 3D virtual world is employed to verify and eliminate stationary and variable objects to allow the autonomous car to focus on dynamic objects that may cause danger to its driving. This methodology takes advantage of prior knowledge of stationary and variable objects presented in a virtual city and verifies their existence in a real-time scene by matching keypoints between the virtual and real objects. In case of a stationary or variable object that does not exist in the virtual world due to incomplete pre-existing information, this method uses machine learning for object detection. Verified objects are then removed from the real-time image with a combined algorithm using contour detection and class activation map (CAM), which helps to enhance the efficiency and accuracy when recognizing moving objects

Car make and model recognition under limited lighting conditions at night

Car make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when license plate numbers cannot be identified or fake number plates are used. CMMR can also be used when a certain model of a vehicle is required to be automatically identified by cameras. The majority of existing CMMR methods are designed to be used only in daytime when most of the car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. The aim of this work was to identify car make and model at night by using available rear view features. This paper presents a one-class classifier ensemble designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and license plates from the rear view is extracted and used in the recognition process. The majority vote from support vector machine, decision tree, and k-nearest neighbors is applied to verify a target model in the classification process. The experiments on 421 car makes and models captured under limited lighting conditions at night show the classification accuracy rate at about 93 %

Car make and model recognition system using rear-lamp features and convolutional neural networks

Recognizing cars based on their features is a difficult task. We propose a solution that uses a convolutional neural network (CNN) and image binarization method for car make and model classification. Unlike many previous works in this area, we use a feature extraction method combined with a binarization method. In the first stage of the pre-processing part we normalize and change the size of an image. The image is then used to recognize where the rear-lamps are placed on the image. We extract the region and use the image binarization method. The binarized image is used as input to the CNN network that finds the features of a specific car model. We have tested the combinations of three different neural network architectures and eight binarization methods. The convolutional neural network with parameters of the highest quality metrics value is used to find the characteristics of the rear lamps on the binary image. The convolutional network is tested with four different gradient algorithms. We have tested the method on two data sets which differ in the way the images were taken. Each data set consists of three subsets of the same car, but is scaled to different image dimensions. Compared to related works that are based on CNN, we use rear view images in different position and light exposure. The proposed method gives better results compared to most available methods. It is also less complex, and faster to train compared to other methods. The proposed approach achieves an average accuracy of 93,9% on the first data set and 84,5% on the second set

Vehicle make and model identification using vision system

Cet article présente un système de reconnaissance du type (constructeur, modèle) de véhicules par vision. À partir d’une vue de face avant d’un véhicule, limitée à sa calandre, nous en construisons une représentation à base de points de contour orientés. La classification est réalisée essentiellement en se fondant sur des algorithmes de votes. L’utilisation d’algorithmes de votes permet au système d’être robuste aux données manquantes ou erronées de la représentation. Nous avons donc construit une fonction de discrimination qui combine 3 votes et une distance, et agit comme une mesure de similarité entre chaque modèle et l’image de véhicule testée. Deux stratégies de décision ont été testées. La première associe à une image de calandre avant du véhicule, le modèle qui a obtenu la valeur la plus importante en sortie de la fonction. Une seconde stratégie regroupe toutes les sorties en un vecteur. La décision est alors prise via un algorithme de plus proche voisin dans un espace dit de votes. Avec la première stratégie, un taux de reconnaissance de 93 % est obtenu sur une base d’images prises en conditions réelles composée de 20 classes de type de véhicules. De plus, une caractérisation et une analyse du fonctionnement du système vis-à-vis de ses différents paramètres est proposée. Cependant ce taux chute à 80 % lorsque le nombre de modèles passe à 50 classes. Pour le même nombre de classes, la seconde stratégie permet d’obtenir un taux supérieur à 90 %.Many vision based Intelligent Transport Systems are dedicated to detect, track or recognize vehicles in image sequences. Three main applications can be distinguished. Firstly, embedded cameras allow to detect obstacles and to compute distances from the equiped vehicle. Secondly, road monitoring measures traffic flow, notifies the health services in case of an accident or informes the police in case of a driving fault. Finally, Vehicle based access control systems for buildings or outdoor sites have to authentify incoming (or outcoming) cars. Rather than these two systems, the third one uses often only the recognition of a small part of vehicle: the license plate. It is enough to identify a vehicle, but in practice the vision based number plate recognition system can provide a wrong information, due to a poor image quality or a fake plate. Combining such systems with others process dedicated to identify vehicle type (brand and model) the authentication can be increased in robustness. This paper adresses the identification problem of a vehicle type from a vehicle greyscale frontal image: the input of the system is an unknown vehicle class, that the system has to determine from a data base. This multiclass recognition system is developed using the oriented-contour pixels to represent each vehicle class. The system analyses a vehicle frontal view identifying the instance as the most similar model class in the data base. The classification is based on voting process and a Euclidean edge distance. The algorithm have to deal with partial occlusions. Tollgates hide a part of the vehicle and making inadequate the appearance-based methods. In spite of tollgate presence, our system doesn’t have to change the training base or apply time-consuming reconstruction process.Fil: Clady, Xavier. Université Pierre et Marie Curie-Paris 6. Institut des Systèmes Intelligents et de Robotique; FranciaFil: Negri, Pablo Augusto. Université Pierre et Marie Curie-Paris 6. Institut des Systèmes Intelligents et de Robotique; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Milgram, Maurice. Université Pierre et Marie Curie-Paris 6. Institut des Systèmes Intelligents et de Robotique; FranciaFil: Poulenard, Raphael. LPREditor; Franci

Car make and model recognition under limited lighting conditions at night

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyCar make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when licence plate numbers cannot be identified or fake number plates are used. CMMR can also be used when automatic identification of a certain model of a vehicle by camera is required. The majority of existing CMMR methods are designed to be used only in daytime when most car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. This work identifies car make and model at night by using available rear view features. A binary classifier ensemble is presented, designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and licence plates from the rear view are extracted and used in the recognition process. The majority vote of individual classifiers, support vector machine, decision tree, and k-nearest neighbours is applied to verify a target model in the classification process. The experiments on 100 car makes and models captured under limited lighting conditions at night against about 400 other car models show average high classification accuracy about 93%. The classification accuracy of the presented technique, 93%, is a bit lower than the daytime technique, as reported at 98 % tested on 21 CMMs (Zhang, 2013). However, with the limitation of car appearances at night, the classification accuracy of the car appearances gained from the technique used in this study is satisfied

Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness

In this modern era, land transports are increasing dramatically. Moreover, self-driven car or the Advanced Driving Assistance System (ADAS) is now the public demand. For these types of cars, road conditions detection is mandatory. On the other hand, compared to the number of vehicles, to increase the number of roads is not possible. Software is the only alternative solution. Road Conditions Detection system will help to solve the issues. For solving this problem, Image processing, and machine learning have been applied to develop a project namely, Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness. Many issues could be considered for road conditions but the main focus will be on the detection of potholes, Maintenance sings and lane. Image processing and machine learning have been combined for our system for detecting in real-time. Machine learning has been applied to maintains signs detection. Image processing has been applied for detecting lanes and potholes. The detection system will provide a lane mark with colored lines, the pothole will be a marker with a red rectangular box and for a road Maintenance sign, the system will also provide information of aintenance sign as maintenance sing is detected. By observing all these scenarios, the driver will realize the road condition. On the other hand situation awareness is the ability to perceive information from it’s surrounding, takes decisions based on perceived information and it makes decision based on prediction