Conteo de vehículos a partir de vídeos usando machine learning

This work presents a framework for vehicle counting from videos, using deep neural networks as detectors. The framework has 4 stages: preprocessing, detection and classification, tracking, and post-processing. For the detection stage, several deep object detector are compared and 3 new ones are proposed based on Tiny YOLOv3. For the tracking, a new tracker based on IOU is compared against the classic ones: Boosting, KCF, TLD, Mediaflow, MOSSE and CSRT. The comparison is based on 8 multi-object tracking metrics over the Bog19 dataset. The Bog19 dataset is a collection of annotated videos from the city of Bogota. The annotations include bicycles, buses, cars, motorbikes and trucks. Finally, the system is evaluated for the task of vehicle counting on this dataset. For the counting task, the combinations of the proposed detectors with the Medianflow and MOSSE trackers obtain the best results. The founded detectors have the same performance as those of the state of the art but with a higher speed.Este trabajo presenta un framework para el conteo de vehı́culos a partir de videos, utilizando redes neuronales profundas como detectores. El framework tiene 4 etapas: preprocesamiento, detección y clasificación, seguimiento y post-procesamiento. Para la etapa de detección se comparan varios detectores de objetos profundos y se proponen 3 nuevos basados en Tiny YOLOv3. Para el rastreo, se compara un nuevo rastreador basado en IOU con los clásicos: Boosting, KCF, TLD, Mediaflow, MOSSE y CSRT. La comparación se hace en base a 8 métricas de seguimiento multiobjeto sobre el conjunto de datos del Bog19. El conjunto de datos Bog19 es una colección de videos anotados de la ciudad de Bogotá. Las clases de objetos anotados incluyen bicicletas, autobuses, coches, motos y camiones. Finalmente el sistema es evaluado para la tarea de contar vehı́culos en este conjunto de datos. Para la tarea de conteo, las combinaciones de los detectores propuestos y los rastreadores Medianflow y MOSSE obtienen los mejores resultados. Los detectores encontrados tienen el mismo desempeño que los del estado del arte pero con una mayor velocidad.Magíster en Ingeniería - Ingeniería de Sistemas y ComputaciónMaestrí

Origin-Destination (O-D) Trip Table Estimation Using Traffic Movement Counts from Vehicle Tracking System at Intersection

A new video-based vehicle tracking system is proposed to provide accurate information on directional traffic counts at intersections. The extracted counts are fed to estimate an origin- destination trip table which is necessary information for traffic impact study and transportation planning. The system utilizes a fisheye lens to expand the area covered by a single camera and uses a particle filtering method to track an individual vehicle. The filter is designed to handle environmental changes and multiple motion dynamics. Experimental results show its strong ability on tracking in various conditions. The paper shows how to use the tracking outputs in obtaining accurate origin-destination (O-D) table

Vision-based vehicle detection and tracking in intelligent transportation system

This thesis aims to realize vision-based vehicle detection and tracking in the Intelligent Transportation System. First, it introduces the methods for vehicle detection and tracking. Next, it establishes the sensor fusion framework of the system, including dynamic model and sensor model. Then, it simulates the traffic scene at a crossroad by a driving simulator, where the research target is one single car, and the traffic scene is ideal. YOLO Neural Network is applied to the image sequence for vehicle detection. Kalman filter method, extended Kalman filter method, and particle filter method are utilized and compared for vehicle tracking. The Following part is the practical experiment where there are multiple vehicles at the same time, and the traffic scene is in real life with various interference factors. YOLO Neural Network combined with OpenCV is adopted to realize real-time vehicle detection. Kalman filter and extended Kalman filter are applied for vehicle tracking; an identification algorithm is proposed to solve the occlusion of the vehicles. The effects of process noise as well as measurement noise are analysed using variable-controlling approach. Additionally, perspective transformation is illustrated and implemented to transfer the coordinates from the image plane to the ground plane. If the vision-based vehicle detection and tracking can be realized and popularized in daily lives, vehicle information can be shared among infrastructures, vehicles, and users, so as to build interactions inside the Intelligent Transportation System

Identification and Classification of Moving Vehicles on Road

It is important to know the road traffic density real time especially in cities for signal control and effective traffic management. In recent years, video monitoring and surveillance systems have been widely used in traffic management. Hence, traffic density estimation and vehicle classification can be achieved using video monitoring systems. The image sequences for traffic scenes are recorded by a stationary camera. The method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Background subtraction is used which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments. The resulting system robustly identifies vehicles, rejecting background and tracks vehicles over a specific period of time. Once the (object) vehicle is tracked, the attributes of the vehicle like width, length, perimeter, area etc are extracted by image process feature extraction techniques. These features will be used in classification of vehicle as big or small using neural networks classification technique of data mining. In proposed system we use LABVIEW and Vision assistant module for image processing and feature extraction.  A feed-forward neural network is trained to classify vehicles using data mining WEKA toolbox. The system will solve major problems of human effort and errors in traffic monitoring and time consumption in conducting survey and analysis of data. The project will benefit to reduce cost of traffic monitoring system and complete automation of traffic monitoring system. Keywords: Image processing, Feature extraction, Segmentation, Threshold, Filter, Morphology, Blob, LABVIEW, NI, VI, Vision assistant, Data mining, Machine learning, Neural network, Back propagation, Multi layer perception, Classification, WEK

Visual Traffic Movement Counts at Intersection for Origin-Destination (O-D) Trip Table Estimation

Origin-destination (O-D) trip table is necessary information for transportation planning and traffic impact study. However, current O-D estimations rely on estimated directional traffic counts at intersections, which obviously diminish reliability in the table. A video-based vehicle tracking system that utilizes wide view-angle lenses has been studied [26] to provide accurate direction traffic counts. The system expands the area covered by a single camera and uses a particle filtering method to handle environmental changes as well as geometric distortion caused by those lenses. This paper shows tracking capability of the system and also shows how to incorporate the directional traffic counts in the O-D estimation