Cities worldwide use camera systems that collect and store large amounts of images, which are used to study vehicle traffic conditions, facilitating traffic management authorities’ decision-making. Typically, the inspection of those images is performed manually, which prevents extracting relevant information in a timely manner. There is a lack of platforms to collect and analyze key data from traffic videos in an automatic and speedy way. Computer vision can be used in combination with parallel distributed systems to provide city authorities tools for automatic and fast processing of stored videos to determine the most significant driving patterns that cause traffic accidents while allowing to measure the traffic density. We use a Convolutional Neural Network (CNN) to detect vehicles captured by traffic cameras, which are then tracked using an algorithm that we designed, based on multi-tracking Kalman filters. To speed up analysis, we propose a low-cost distributed infrastructure based on Hadoop and Spark frameworks for data processing: videos are equally divided and distributed to multicore CPU nodes for analysis. However, splitting up videos could generate inaccuracies in vehicle counting, which were avoided through the use of an algorithm that we present in this work. We found that it is possible to rapidly determine traffic densities, identify dangerous driving maneuvers, and detect accidents with high accuracy by using low-cost commodity cluster computing. There is a lack of computing platforms to collect and analyze key data from traffic videos in an automatic and speedy way. Computer vision can be used in combination with parallel distributed systems to provide city authorities tools for automatic and fast processing of stored videos to determine the most significant driving patterns that cause traffic accidents while allowing to measure the traffic density. This study explores the integration of different tools such as parallel data processing, deep learning, and probabilistic models. We present an approach based on Convolutional Neural Network (CNN) and Kalman filters to detect and track vehicles captured by traffic cameras. To speed up analysis, we propose and evaluate a low-cost distributed infrastructure based on Hadoop and Spark frameworks and comprised of multicore CPU nodes for data processing. Finally, we present an algorithm to allow vehicle counting while avoiding inaccuracies generated when videos are split to be distributed for analysis. We found that it is possible to rapidly determine traffic densities, identify dangerous driving maneuvers, and detect accidents with high accuracy by using low-cost commodity cluster computing.

Road accidents is one of the major cause of deaths worldwide. Traffic is also one of the main problems in large cities. The development of intelligent transportation systems may contribute to the solution of both problems. Autonomous vehicles have been addressed by the academic community for decades and recently have been obtaining a considerable attention from the industry. One of the next steps for such technology is the development of communication systems that allow cooperative driving, which could improve the traffic efficiency. This paper proposes a simulation framework for the development of cooperative driving systems. Our framework is capable of accurate simulation of vehicles, sensors, complex environments, and wireless communication between vehicles. It is also compatible to well known robotic control middlewares, allowing portability of the develop code between real and simulated test vehicles. Results of cooperative adaptive cruise control with multiple vehicles are presented to validate the framework proposed.

Dendrometry refers to the study of the geometrical dimensions of a tree as an individual entity. This paper presents a dendrometric feature estimation pipeline to calculate the tree height, diameter at breast height (DBH) and the height to the base of the crown (trunk height), taking advantage of the abilities of photogrammetric systems that produce spatially accurate 3D models using a sets of overlapping digital images and in this way, provide a technological dendrometric measurement technique. Tests were carried out on 5 trees. The corresponding 3D models were obtained, then each model was rescaled to a real measure, the data set belonging to the trunk and the crown of the tree were identified and finally, the total tree height, the height at the base of the crown and the diameter at breast height were estimated.

Alignment of pair wise image point clouds is an important task in building environment maps with partial information. The combination of depth information and images provided by RGB-D cameras are often used to improve such alignment. However, when the environment is structured and its images show little texture, depth information is more reliable, on the other hand, when the images of the environment have enough texture, better results are achieved when texture information is used. In this paper, we propose a new adaptive approach to make the most effective selection of image or depth information in order to find a better alignment of points and thus better define the rigid transformation between two point clouds. Our approach uses an adaptive parameter based on the degree of texture of the scene, selecting not only FPFH and SURF descriptors, but also weighting the iterative ICP process. Datasets containing RGB-D data with textured and non textured images are used to validate our proposal.

Cooperative driving systems are a possible solution to improve traffic efficiency, road safety and even reduce the emission of pollutants, but there are several problems relating to aspects of safety, cost, duration and feasibility that slow down the development and experiments. In this context, simulation tools are essential to help researchers on this development. The goal of this paper is to present our simulator framework for cooperative vehicle systems and also a test case for validation. This framework is an integration among open-source tools which is composed by real-time robotics simulator and network simulator. The test case is a simulation which implements the Cooperative Adaptive Cruise Control (CACC) that uses both vehicle control and communication. The simulation is based on an approach Leader -- Follower with four vehicles, using a Preceding Communication Topology. The proposed Framework allows evaluating the delay time effect on CACC communication systems and also validating the control performance, taken into consideration the distance and velocity of the simulated vehicles.