Auto Detection of Number Plate of Person without Helmet

Automated Number Plate Recognition organization would greatly enhance the ability of police to detect criminal commotion that involves the use of motor vehicles. Automatic video investigation from traffic surveillance cameras is a fast-emerging field based on workstation vision techniques. It is a key technology to public safety, intelligent transport system (ITS) and for efficient administration of traffic without wearing helmet. In recent years, there has been an increased scope for involuntary analysis of traffic activity. It defines video analytics as computer-vision-based supervision algorithms and systems to extract contextual information from video. In traffic circumstancesnumeroussupervise objectives can be continue by the application of computer vision and pattern gratitude techniques, including the recognition of traffic violations (e.g., illegal turns and one-way streets) and the classification of road users (e.g., vehicles, motorbikes, and pedestrians). Currently most reliable approach is through the acknowledgment of number plates, i.e., automatic number plate recognition (ANPR)

Advanced Moving Object Detection and Tracking for Video Surveillance

Moving object detection is a very crucial and challenging task in computer vision applications such as surveillance, vehicle and human tracking. Background subtraction is a preliminary technique widely used for the moving object detection. In this paper, an advanced automated moving object detection technique using background subtraction is proposed. The method uses running average wavelet transform (RAWT) for accurate registration of background from the video sequence. Furthermore, the moving objects are detected by comparing current and background frame. In order to produce higher accuracy for the object detection, the proposed method also further includes post-processing filter operation after which the binary object detection mask can be obtained. After moving object detection, tracking is performed. Experimental results demonstrate that the proposed method is faster and efficient as compared to the other state-of-the-art existing methods

Multithreading Application for Counting Vehicle by Using Background Subtraction Method

Image and video processing has become important part in intelligent transportation system (ITS) application, especially for collecting road traffic data. Pictures that already collected by a charged coupled device (CCD) camera usually being processed by several image processing algorithms and the application’s code will be executed in a large number of iteration because many algorithms are getting involved in processing the frame which captured by the camera. Typical application will process the first frame until finish and then continue to the next frame, so the application must wait until the first frame being processed. If the algorithms that executed quite complex and have a significant running time there will be a dropped frame and the time difference between data acquisition and real time video is divided by large margin. We proposed an implementation of multithreading to boost the application performance so the data can be acquire in real time and every new frame could be processed in short time. The application performance before and after using a multithreading is known by comparing the data acquisition time that stored in the database. The application effectiveness could define by running a multiple video streaming in same resolution

Real Time Automatic Number Plate Recognition Using Morphological Algorithm

The rising increase of up to date urban and national road networks over the last three decades become known the need of capable monitoring and management of road traffic. Expected techniques for traffic measurements, such as inductive loops, sensors or EM microwave detectors, endure from sober shortcomings, luxurious to install, they demand traffic distraction during installation or maintenance, they are massive and they are unable to notice slow or momentary stop vehicles. On the divergent, systems that are based on video are simple to install, use the existing infrastructure of traffic observation. Currently most reliable method is through the detection of number plates, i.e., automatic number plate recognition (ANPR), which is also branded as automatic license plate recognition (ALPR), or radio frequency transponders. The first revalent step of information is finding of moving objects in video streams and background subtraction is a very accepted approach for foreground segmentation. Next step is License plate extraction which is an essential stage in license plate recognition for automatic transport system. We are planned for two ways for removal of license plates and comparing it with other existing methods. The Extracted license plates are segmented into particular characters by means of a region-based manner. The recognition scheme unites adaptive iterative thresholding with a template matching algorithm. The method is strong to illumination, character size and thickness, skew and small character breaks. The main reward of this system is its real-time capability and that it does not require any extra sensor input (e.g. from infrared sensors) except a video stream. This system is judged on a huge number of vehicle images and videos. The system is also computationally extremely efficient and it is appropriate for others related image recognition applications. This system has broad choice of applications such as access control, ringing, border patrol, traffic control, finding stolen cars, etc. Furthermore, this technology does not need any fitting on cars, such as transmitter or responder

Computer Vision Techniques for Background Modeling in Urban Traffic Monitoring

Jose Manuel Milla, Sergio Luis Toral, Manuel Vargas and Federico Barrero (2010). Computer Vision Techniques for Background Modeling in Urban Traffic Monitoring, Urban Transport and Hybrid Vehicles, Seref Soylu (Ed.), ISBN: 978-953-307-100-8, InTech, DOI: 10.5772/10179. Available from: http://www.intechopen.com/books/urban-transport-and-hybrid-vehicles/computer-vision-techniques-for-background-modeling-in-urban-traffic-monitoringIn this chapter, several background modelling techniques have been described, analyzed and tested. In particular, different algorithms based on sigma-delta filter have been considered due to their suitability for embedded systems, where computational limitations affect a real-time implementation. A qualitative and a quantitative comparison have been performed among the different algorithms. Obtained results show that the sigma-delta algorithm with confidence measurement exhibits the best performance in terms of adaptation to particular specificities of urban traffic scenes and in terms of computational requirements. A prototype based on an ARM processor has been implemented to test the different versions of the sigma-delta algorithm and to illustrate several applications related to vehicle traffic monitoring and implementation details

Automatic Vehicle Detection, Tracking and Recognition of License Plate in Real Time Videos

Automatic video analysis from traffic surveillance cameras is a fast-emerging field based on computer vision techniques. It is a key technology to public safety, intelligent transport system (ITS) and for efficient management of traffic. In recent years, there has been an increased scope for automatic analysis of traffic activity. We define video analytics as computer-vision-based surveillance algorithms and systems to extract contextual information from video. In traffic scenarios several monitoring objectives can be supported by the application of computer vision and pattern recognition techniques, including the detection of traffic violations (e.g., illegal turns and one-way streets) and the identification of road users (e.g., vehicles, motorbikes, and pedestrians). Currently most reliable approach is through the recognition of number plates, i.e., automatic number plate recognition (ANPR), which is also known as automatic license plate recognition (ALPR), or radio frequency transponders. Here full-featured automatic system for vehicle detection, tracking and license plate recognition is presented. This system has many applications in pattern recognition and machine vision and they ranges from complex security systems to common areas and from parking admission to urban traffic control. This system has complex characteristics due to diverse effects as fog, rain, shadows, uneven illumination conditions, occlusion, variable distances, velocity of car, scene's angle in frame, rotation of plate, number of vehicles in the scene and others. The main objective of this work is to show a system that solves the practical problem of car identification for real scenes. All steps of the process, from video acquisition to optical character recognition are considered to achieve an automatic identification of plates

Extracción y seguimiento de los miembros inferiores durante la marcha

El propósito principal de esta investigación ha sido el desarrollo de métodos que permitan el análisis de la marcha sin uso de marcadores. Los marcadores alteran el gesto natural del movimiento, son incómodos y su ubicación varía entre experimentos sucesivos. El método desarrollado captura el movimiento en el plano sagital mediante una cámara convencional, estima el fondo y segmenta la silueta de los miembros inferiores, y mediante una transformación de distancia que esqueletoniza la silueta, detecta y sigue los puntos articulares que corresponden a la cadera, rodillas y tobillos. El método se evaluó en 22 diferentes videos, 12 capturados en condiciones semicontroladas y 10 videos en condiciones ambientales. Los resultados obtenidos se compararon con aquellos obtenidos por el método convencional (con marcadores), mediante un estimador llamado distancia de Fréchet encontrándose una similitud del 65% entre los desplazamientos angulares de cadera y rodilla. Este método podría ser utilizado en aplicaciones clínicas de análisis de marcha. / Abstract. The main goal of this investigation has been the development of methods which allow markerless gait analysis. Markers alter the natural movement gesture, they are uncomfortable and their location vary between consecutive experiments. The developed method captures the movement in sagittal plane using a conventional camera, estimates the background and segmentates the lower limb silhouette. By means of a distance transformation it skeletonizes the silhouette, detects and tracks the joint points corresponding to the hip, knees and ankles. The method was evaluated in 22 different videos, 10 captured in semi-controlled conditions and 12 in environmental conditions. The obtained results were compared with those obtained by the conventional method (with markers), by means of Fréchet distance estimation, finding a 65% similarity between angular displacements of hip and knee. This method could be easily used in clinical applications.Maestrí

Motion detection, object classification and tracking for visual surveillance application

Visual surveillance in dynamic scenes, especially for humans and vehicles, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism, crime, public safety and for efficient management of traffic. The work involves designing of efficient visual surveillance system in complex environments. In video surveillance, detection of moving objects from a video is important for object classification, target tracking, activity recognition, and behavior understanding. Detection of moving objects in video streams is the first relevant step of information and background subtraction is a very popular approach for foreground segmentation. In this thesis, we have simulated different background subtraction methods to overcome the problem of illumination variation, background clutter, shadows, and camouflage. Object classification is done using silhouette template based classification to categorize objects into human, group of human and vehicle. Detecting and tracking of human body parts is important in understanding human activities. We have proposed two methods to overcome the problem of object tracking in varying illumination condition and background clutter. For target tracking of interested object in the consecutive video frames, we have used normalized correlation coefficient (NCC). NCC is robust to varying illumination condition. Template is updated on every frame to minimize the template drift problem and it also tries to cope with short-lived occlusion and background clutter. In order to extend the surveillance area and overcome occlusion, fusion of data from multiple cameras is employed in our project. We have tracked objects across multiple cameras with non-overlapping FOVs based on object appearances. A brightness transfer function (BTF) is determined from the cumulative histograms of the images. Matching of the object is done, with the help of Bhattacharya distance

Latent Dependency Mining for Solving Regression Problems in Computer Vision

PhDRegression-based frameworks, learning the direct mapping between low-level imagery features and vector/scalar-formed continuous labels, have been widely exploited in computer vision, e.g. in crowd counting, age estimation and human pose estimation. In the last decade, many efforts have been dedicated by researchers in computer vision for better regression fitting. Nevertheless, solving these computer vision problems with regression frameworks remained a formidable challenge due to 1) feature variation and 2) imbalance and sparse data. On one hand, large feature variation can be caused by the changes of extrinsic conditions (i.e. images are taken under different lighting condition and viewing angles) and also intrinsic conditions (e.g. different aging process of different persons in age estimation and inter-object occlusion in crowd density estimation). On the other hand, imbalanced and sparse data distributions can also have an important effect on regression performance. Apparently, these two challenges existing in regression learning are related in the sense that the feature inconsistency problem is compounded by sparse and imbalanced training data and vice versa, and they need be tackled jointly in modelling and explicitly in representation. This thesis firstly mines an intermediary feature representation consisting of concatenating spatially localised feature for sharing the information from neighbouring localised cells in the frames. This thesis secondly introduces the cumulative attribute concept constructed for learning a regression model by exploiting the latent cumulative dependent nature of label space in regression, in the application of facial age and crowd density estimation. The thesis thirdly demonstrates the effectiveness of a discriminative structured-output regression framework to learn the inherent latent correlation between each element of output variables in the application of 2D human upper body pose estimation. The effectiveness of the proposed regression frameworks for crowd counting, age estimation, and human pose estimation is validated with public benchmarks

Proposta de um Sistema de Tomada de Decisão para Detecção de Veículos em Movimento para FPGA

Os métodos pesquisados para detecção de objetos em movimento através do processamento de imagens em processadores de uso geral (General Purpose Processors - GPPs) apresentam, em sua maioria, uma abordagem que não permite uma implementação com bons resultados em matriz de portas programável em campo (Field Programmable Gate Array-FPGA). Isso ocorre devido à classificação correta dos pixels estar diretamente relacionada à implementação de técnicas mais complexas para modelar a imagem de referência e que requerem muitos recursos em termos de memória. Além disso, quase todos os métodos analisados realizam apenas o processamento da tomada de decisão clássica, sendo poucas as propostas que baseiam sua tomada de decisão na integral fuzzy. Assim, visando melhorar a classificação dos pixels durante o processo de detecção de veículos em movimento é proposta uma abordagem que realiza a fusão das tomadas de decisão fuzzy e clássica combinando técnicas convencionais de processamento digital de imagens. Dessa forma, o sistema de tomada de decisão proposto para detectar os veículos em movimento busca não comprometer os resultados em termos de classificação dos pixels mesmo utilizando um a técnica de modelagem simples para obter a imagem de referência. Essa imagem é obtida através da estimativa do valor mediano e possibilita que o sistema de detecção de veículos em movimento proposto não precise do armazenamento de várias imagens para obter a imagem de referência. Os resultados são verificados em termos de recursos ocupados, frequência máxima de operação e classificação dos pixels em FPGAs de baixo custo. Além disso, os resultados em termos de classificação dos pixels são comparados através de várias medidas com outros métodos, apresentando resultados promissores no processamento de imagens em tempo real em FPGAs de baixo custo