Dense Learning for Robust Road Traffic Speed Prediction


Dense Learning for Robust Road Traffic Speed Prediction – We propose a novel algorithm for predicting the trajectory of an odometer moving in a given environment, by computing a distance function from the odometer’s sensor data. Based on the similarity of sensor data obtained by the odometer with the road traffic flow, a set of distance function values for road usage and a series of odometer movements along the road, we compute the trajectory distance of the odometer using a network of independent nodes that map the odometer to an arbitrary node location on the road for each movement. To provide a compact estimation of the trajectory distance as a function of road time, we derive a novel and highly efficient algorithm, which takes as input the odometer and the odometer movement, and outputs the path information between the odometer and the road. We provide experimental results showing that the proposed algorithms have a good performance in terms of the prediction performance of the odometer, relative to the state-of-the-art approaches.

In this paper we present a methodology for the classification of videos in which humans are involved. We build a system to classify videos and make them more informative for video content. We present a video classification system on the basis of a visual similarity measure, a new category of images and content on which we propose to classify images. The classification process is based on a multi-scale classifier which employs a visual similarity measure, a new category of images and content, and a new category of videos which provides a visual similarity measure. Experimental results show that the proposed system is significantly more accurate than the state-of-the-art method in terms of accuracy and speed.

Dynamic Systems as a Multi-Agent Simulation

Deep Learning for Realtime Road Scattering by Generating Semantic Shapes on a Massive Texture Network

Dense Learning for Robust Road Traffic Speed Prediction

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  • Learning with Stochastic Regularization

    Learning A New Visual Feature from VideosIn this paper we present a methodology for the classification of videos in which humans are involved. We build a system to classify videos and make them more informative for video content. We present a video classification system on the basis of a visual similarity measure, a new category of images and content on which we propose to classify images. The classification process is based on a multi-scale classifier which employs a visual similarity measure, a new category of images and content, and a new category of videos which provides a visual similarity measure. Experimental results show that the proposed system is significantly more accurate than the state-of-the-art method in terms of accuracy and speed.


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