AUTOMATED VEHICLE COUNTING AND CLASSIFICATION SYSTEM FOR TRAFFIC CENSUS

Traffic census is important for the purpose of upgrading and widening the road. The information gained from the traffic census can be used in the budget planning for road maintenance. Traffic census can be done automatically or by counting and classifying the vehicles manually using human labor. Most of the automatic traffic census system used nowadays focus on counting the vehicles by using devices called magnetic loop detector. This device is costly and once installed, it cannot be removed. To overcome this problem, an automated traffic census system based on image processing is introduced which can be used to count and to classify the classes of the vehicle. Computer vision technology is used to achieve this objective. For the vehicle detection, background subtraction and approximate median algorithm are used. The system uses the length of the vehicle for the purpose of classification. The chosen algorithm for vehicle detection is called approximate median as it is more accurate compared to background subtraction method. On the other hand, although the results gained by using approximate median method is more accurate than a simple background subtraction method, it has its drawback too which is more complex calculation hence taking more time to execute the algorithm. Some optimizations have been done on the approximate median algorithm and the result is very promising as it has shortened the execution time while the accuracy of the detection remains the same. In conclusion, this project is a success since it can count and classify the vehicles, but further works need to be done to achieve better accuracy

AUTOMATED VEHICLE COUNTING AND CLASSIFICATION SYSTEM FOR TRAFFIC CENSUS

Traffic census is important for the purpose of upgrading and widening the road. The information gained from the traffic census can be used in the budget planning for road maintenance. Traffic census can be done automatically or by counting and classifying the vehicles manually using human labor. Most of the automatic traffic census system used nowadays focus on counting the vehicles by using devices called magnetic loop detector. This device is costly and once installed, it cannot be removed. To overcome this problem, an automated traffic census system based on image processing is introduced which can be used to count and to classify the classes of the vehicle. Computer vision technology is used to achieve this objective. For the vehicle detection, background subtraction and approximate median algorithm are used. The system uses the length of the vehicle for the purpose of classification. The chosen algorithm for vehicle detection is called approximate median as it is more accurate compared to background subtraction method. On the other hand, although the results gained by using approximate median method is more accurate than a simple background subtraction method, it has its drawback too which is more complex calculation hence taking more time to execute the algorithm. Some optimizations have been done on the approximate median algorithm and the result is very promising as it has shortened the execution time while the accuracy of the detection remains the same. In conclusion, this project is a success since it can count and classify the vehicles, but further works need to be done to achieve better accuracy

Sistema automático de estimación de densidad de tráfico en imágenes de videovigilancia en carretera

Se ha creado un sistema de validación experimental para sistemas de estimación de la congestión del tráfico. El objetivo principal del trabajo ha sido la implementación y evaluación de un sistema de estimación precisa del número de vehículos presentes en la escena. Para ello, en lugar de detectar y localizar la posición individual de los objetos, se ha planteado un esquema que es capaz de estimar la densidad de los vehículos, de modo que, una vez obtenida la misma, la cuenta de los objetos presentes en una zona de la imagen puede aproximarse computando la integral de la densidad estimada. Para la evaluación experimental, se ha utilizado una base de datos que hemos recopilado expresamente para este proyecto. En ella se incluyen imágenes reales, tomadas por cámaras de videovigilancia de tráfico, que han sido debidamente anotadas para evaluar las soluciones. A la vista de los resultados obtenidos, destacamos dos conclusiones. La primera es que las imágenes recopiladas suponen un gran desafío, incluso para sistemas considerados como el estado del arte en cuanto a conteo de objetos se refiere. La segunda es que para obtener unos resultados satisfactorios con el modelo implementado, resulta fundamental realizar un ajuste de los parámetros del sistema.In this work, we have developed a system for estimating the number of vehicles in images of video surveillance cameras. The goal of this project is the implementation and experimental evaluation of a system for accurately estimating the number of vehicles on the scene. For this purpose, instead of detecting and locating the position of individual objects, we have proposed a system that is able to estimate the density of the vehicles. When the estimated density is obtained, the count of the objects present in any region of the image can be approximated by computing the integral over the estimated density. For the experimental evaluation, we have used a database specifically built for this project. It includes real images taken by traffic video surveillance cameras. These images have been properly annotated for evaluating the solutions. Regarding to the results, we highlight two conclusions. The first one is that the images collected represent a big challenge, even for systems considered the state-of- the- art in terms of counting objects. The second one is that to obtain satisfactory results with the implemented model, it is essential to make an adjustment of the system parameters.Grado en Ingeniería en Sistemas de Telecomunicació

Sistema automático de estimación de densidad de tráfico en imágenes de videovigilancia en carretera

Se ha creado un sistema de validación experimental para sistemas de estimación de la congestión del tráfico. El objetivo principal del trabajo ha sido la implementación y evaluación de un sistema de estimación precisa del número de vehículos presentes en la escena. Para ello, en lugar de detectar y localizar la posición individual de los objetos, se ha planteado un esquema que es capaz de estimar la densidad de los vehículos, de modo que, una vez obtenida la misma, la cuenta de los objetos presentes en una zona de la imagen puede aproximarse computando la integral de la densidad estimada. Para la evaluación experimental, se ha utilizado una base de datos que hemos recopilado expresamente para este proyecto. En ella se incluyen imágenes reales, tomadas por cámaras de videovigilancia de tráfico, que han sido debidamente anotadas para evaluar las soluciones. A la vista de los resultados obtenidos, destacamos dos conclusiones. La primera es que las imágenes recopiladas suponen un gran desafío, incluso para sistemas considerados como el estado del arte en cuanto a conteo de objetos se refiere. La segunda es que para obtener unos resultados satisfactorios con el modelo implementado, resulta fundamental realizar un ajuste de los parámetros del sistema.In this work, we have developed a system for estimating the number of vehicles in\ud images of video surveillance cameras. The goal of this project is the implementation and\ud experimental evaluation of a system for accurately estimating the number of vehicles on\ud the scene. For this purpose, instead of detecting and locating the position of individual\ud objects, we have proposed a system that is able to estimate the density of the vehicles.\ud When the estimated density is obtained, the count of the objects present in any region of\ud the image can be approximated by computing the integral over the estimated density.\ud For the experimental evaluation, we have used a database specifically built for this\ud project. It includes real images taken by traffic video surveillance cameras. These images\ud have been properly annotated for evaluating the solutions. Regarding to the results,\ud we highlight two conclusions. The first one is that the images collected represent a big\ud challenge, even for systems considered the state-of- the- art in terms of counting objects.\ud The second one is that to obtain satisfactory results with the implemented model, it is\ud essential to make an adjustment of the system parameters.Grado en Ingeniería en Sistemas de Telecomunicació