14,359 research outputs found

    Highway traffic monitoring on medium resolution satellite images

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    International audienceThese last years, earth observation imagery has significantly improved. Public satellites such as WorldView-3 can now produce images with a Ground Sample Distance of 31cm, reaching an equivalent resolution than aerial images. Perhaps more importantly, the revisit frequency has also been greatly enhanced: providers such as Planet can now acquire images of an area on a daily basis. These major improvements are fueled by an increasing demand for frequent objects detection. An application generating a particular interest is vehicle detection. Indeed, vehicle detection can give to public and private actors valuable data such as traffic monitoring and parking occupancy rate estimations. Several datasets, such as DOTA or VehSat, already exist, allowing researchers to train machine learning algorithms to detect vehicles. However, these datasets focus on relatively high definition and expensive aerial and satellite images. In this paper, we will present a method for detecting vehicles on medium resolution satellite images, with a GSD comprised between 1 and 5 meters. This approach can notably be used on Planet images, allowing to monitor traffic of an area on a daily basis

    Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

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    Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation

    Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps

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    Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications. In this work, we present a methods that uses a deep convolutional neural network (CNN) to infer whether grid cells are covering a moving object or not. Compared to tracking approaches, that use e.g. a particle filter to estimate grid cell velocities and then make a decision for individual grid cells based on this estimate, our approach uses the entire grid map as input image for a CNN that inspects a larger area around each cell and thus takes the structural appearance in the grid map into account to make a decision. Compared to our reference method, our concept yields a performance increase from 83.9% to 97.2%. A runtime optimized version of our approach yields similar improvements with an execution time of just 10 milliseconds.Comment: This is a shorter version of the masters thesis of Florian Piewak and it was accapted at IV 201
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