A Neutral Network Based Vehicle Classification System for Pervasive Smart Road Security

Pervasive smart computing environments make people get accustomed to convenient and secure services. The overall goal of this research is to classify vehicles along the I215 freeway in Salt Lake City, USA. This information will be used to predict future roadway needs and the expected life of a roadway. The classification of vehicles will be performed by a synthesis of multiple sets of features. All feature sets have not yet been determined; however, one such set will be the reduced wavelet transform of the image of a vehicle. In order to use such a feature, it is necessary that the image be normalized with respect to size, position, and so on. For example, a car in the right most lane in an image will appear smaller than one in the left most lane, because the right most lane is closest to the camera. Likewise, a vehicle’s size will vary depending on where in a lane its image is captured. In our case, the image capture area for each lane is approximately 100 feet of roadway. A goal of this paper is to normalize the image of a vehicle so that regardless of its lane or position in a lane, the features will be approximately the same. The wavelet transform itself will not be used directly for recognition. Instead, it will be input to a neural network and the output of the neural network will be one element of the feature set used for recognition

Evaluating the accuracy of vehicle tracking data obtained from Unmanned Aerial Vehicles

Abstract This paper presents a methodology for tracking moving vehicles that integrates Unmanned Aerial Vehicles with video processing techniques. The authors investigated the usefulness of Unmanned Aerial Vehicles to capture reliable individual vehicle data by using GPS technology as a benchmark. A video processing algorithm for vehicles trajectory acquisition is introduced. The algorithm is based on OpenCV libraries. In order to assess the accuracy of the proposed video processing algorithm an instrumented vehicle was equipped with a high precision GPS. The video capture experiments were performed in two case studies. From the field, about 24,000 positioning data were acquired for the analysis. The results of these experiments highlight the versatility of the Unmanned Aerial Vehicles technology combined with video processing technique in monitoring real traffic data

Desarrollo de un prototipo de semáforo inteligente con visión por computador

El proyecto técnico se desarrolló mediante el enfoque de realizar el prototipo de un semáforo inteligente con visión por computador; con un módulo de detección de autos, mediante el cual se cuenta, en tiempo real, los autos de dos vías de una intersección, para dar prioridad a la vía con mayor número de vehículos. Con esta información, con la ayuda de un módulo controlador de semáforo, se determina el tiempo de cambio de luz dinámicamente, priorizando la circulación, a la vía con mayor cantidad de vehículos, disminuyendo la congestión vehicular y demora en la movilidad en dicha intersección. Para programar los módulos de conteo de vehículos y de control del semáforo se utilizó el lenguaje Python, fundamental para el reconocimiento de objetos, y para conseguir desarrollar el módulo de detección de vehículos; una de sus librerías llamada OpenCV, que por medio de la captura y procesamiento de las imágenes obtenidas por la cámara se consigue detectar al vehículo entre los demás objetos para su conteo y con dicha información mediante el módulo controlador del semáforo distribuir el tiempo para el cambio de luz, los datos obtenidos se almacenan en una base de datos, para ello se usó PostgreSQL.The technical project was developed through the approach of making the prototype of an intelligent traffic light with computer vision; with a car detection module, through which two-way cars at an intersection are counted in real time, to give priority to the road with the largest number of vehicles. With this information, with the help of a traffic light controller module, the time of change of light is determined dynamically, giving priority to circulate, to the road with the largest number of vehicles, reducing traffic congestion and delay in mobility at that intersection. To program the vehicle counting and traffic light control modules, the Python language was used, essential for object recognition, and to develop the vehicle detection module; one of its libraries called OpenCV. Capturing and processing the images obtained by the camera is able to detect the vehicle among the other objects for counting and with said information through the traffic light controller module distribute the time for the change of light, the data obtained is stored in a postgres database. To carry out the project, in addition to the research, computer vision has been applied mainly, which is a branch of study of Artificial Intelligence, and which, added to the programming implemented in the modules, makes the developed prototype differ from a conventional traffic light