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检索条件"机构=Department of Computer Science and Engineering—DISI"

共 180 条记录，以下是21-30 订阅

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Predicting social density in mass events to prevent crowd disasters

Predicting social density in mass events to prevent crowd di...

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5th International Conference on Social Informatics, SocInfo 2013

作者： Anzengruber, Bernhard Pianini, Danilo Nieminen, Jussi Ferscha, Alois Institute for Pervasive Computing Johannes Kepler University Linz Austria Department of Computer Science and Engineering - DISI Alma Mater Studiorum Università di Bologna Italy

ISBN: (纸本)9783319032597

Human mobility behavior emerging in social events involving huge masses of individuals bears potential hazards for irrational social densities. We study the emergence of such phenomena in the context of very large public sports events, analyzing how individual mobility decision making induces undesirable mass effects. A time series based approach is followed to predict mobility patterns in crowds of spectators, and related to the event agenda over the time it evolves. Evidence is collected from an experiment conducted in one of the biggest international sports events (the Vienna city marathon with 40.000 actives and around 300.000 spectators). A smartphone app has been developed to voluntarily engage people to provide mobility data (1503 high-quality GPS traces and 1092694 Bluetooth relations have been collected), based on which prediction analysis has been performed. Using this data as training set, we compare density estimation approaches and evaluate them based on their forecasting precision. The most promising approach using Support Vector Regression (SMOreg) achieved prediction accuracies below 2 (root-mean-squared deviation) when compared to actual evidenced density distributions for a 12 minute forecasting interval. © 2013 Springer International Publishing.

关键词： Decision making

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TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network

TemporalStereo: Efficient Spatial-Temporal Stereo Matching N...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Youmin Zhang Matteo Poggi Stefano Mattoccia Department of Computer Science and Engineering (DISI) University of Bologna

We present TemporalStereo, a coarse-to-fine stereo matching network that is highly efficient, and able to effectively exploit the past geometry and context information to boost matching accuracy. Our network leverages sparse cost volume and proves to be effective when a single stereo pair is given. However, its peculiar ability to use spatio-temporal information across stereo sequences allows TemporalStereo to alleviate problems such as occlusions and reflective regions while enjoying high efficiency also in this latter case. Notably, our model - trained once with stereo videos - can run in both single-pair and temporal modes seamlessly. Experiments show that our network relying on camera motion is robust even to dynamic objects when running on videos. We validate TemporalStereo through extensive experiments on synthetic (SceneFlow, TartanAir) and real (KITTI 2012, KITTI 2015) datasets. Our model achieves state-of-the-art performance on any of these datasets.

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Real-time self-adaptive deep stereo

Real-time self-adaptive deep stereo

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Alessio Tonioni Fabio Tosi Matteo Poggi Stefano Mattoccia Luigi di Stefano Department of Computer Science and Engineering (DISI) University of Bologna

ISBN: (纸本)9781728132945

Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs. These models, however, suffer from a notable decrease in accuracy when exposed to scenarios significantly different from the training set (e.g., real vs synthetic images, etc.). We argue that it is extremely unlikely to gather enough samples to achieve effective training/tuning in any target domain, thus making this setup impractical for many applications. Instead, we propose to perform unsupervised and continuous online adaptation of a deep stereo network, which allows for preserving its accuracy in any environment. However, this strategy is extremely computationally demanding and thus prevents real-time inference. We address this issue introducing a new lightweight, yet effective, deep stereo architecture, Modularly ADaptive Network (MADNet), and developing a Modular ADaptation (MAD) algorithm, which independently trains sub-portions of the network. By deploying MADNet together with MAD we introduce the first real-time self-adaptive deep stereo system enabling competitive performance on heterogeneous datasets. Our code is publicly available at .

关键词： market abuse MAD LANGUAGE mycophenolic adenine dinucleotide self-adaptive Acoustic equipment Real time Modularity end-to-end Stereo pairs Codes

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Guided Stereo Matching

Guided Stereo Matching

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Matteo Poggi Davide Pallott Fabio Tosi Stefano Mattoccia Department of Computer Science and Engineering (DISI) University of Bologna

ISBN: (纸本)9781728132945

Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments. Therefore, in this paper, we introduce Guided Stereo Matching, a novel paradigm leveraging a small amount of sparse, yet reliable depth measurements retrieved from an external source enabling to ameliorate this weakness. The additional sparse cues required by our method can be obtained with any strategy (e.g., a LiDAR) and used to enhance features linked to corresponding disparity hypotheses. Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch. Extensive experiments on three standard datasets and two state-of-the-art deep architectures show that even with a small set of sparse input cues, i) the proposed paradigm enables significant improvements to pre-trained networks. Moreover, ii) training from scratch notably increases accuracy and robustness to domain shifts. Finally, iii) it is suited and effective even with traditional stereo algorithms such as SGM.

关键词： Scratch Lidar ARCHITECTURE Training Functional training Enabling STRUCTURE MAP Machine Learning

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Consistency problems in UML-based software development

Consistency problems in UML-based software development

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UML 2004 Satellite Activities

作者： Huzar, Zbigniew Kuzniarz, Ludwik Reggio, Gianna Sourrouille, Jean Louis Department of Computer Science Worclaw University of Technology Worclaw Poland School of Engineering Blekinge Institute of Technology Ronneby Sweden DISI Università di Genova Genova Italy Department of Information Technology and Computer Engineering INSA Lyon France

This survey of the workshop series Consistency Problems in UML-based Software Development aims to help readers to find the guidelines of the papers. First, general considerations about consistency and related problems are discussed. Next, the approaches proposed in the workshop papers to handle the problems are categorized and summarized. The last section includes extended abstracts of the papers from the current workshop. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Software engineering

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Leveraging Confident Points for Accurate Depth Refinement on Embedded Systems

Leveraging Confident Points for Accurate Depth Refinement on...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Fabio Tosi Matteo Poggi Stefano Mattoccia Department of Computer Science and Engineering (DISI) University of Bologna Italy

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Despite the notable progress in stereo disparity estimation, algorithms are still prone to errors in challenging conditions. Thus, heuristic disparity refinement techniques are usually deployed to improve accuracy. Moreover, state-of-the-art methods rely on complex CNNs requiring power hungry GPUs not suited for many practical applications constrained by limited computing resources. In this paper, we propose a novel technique for disparity refinement leveraging on confidence measures and a novel, automatic learning-based selection method to discard outliers. Then, a non-local strategy infers missing disparities by analyzing the closest reliable points. This framework is very fast and does not require any hand-tuned thresholding. We assess the performance of our Non-Local Anchoring (NLA), standalone refinement techniques and methods leveraging on confidence measures inside the stereo algorithm. Our evaluation with two popular stereo algorithms shows that our proposal significantly ameliorates their accuracy on Middlebury v3 and KITTI 2015 datasets. Moreover, since our method relies only on cues computed in the disparity domain, it is suited even for COTS stereo cameras coupled with embedded systems, e.g. nVidia Jetson TX2.

关键词： Reliability Proposals Pipelines Machine learning Embedded systems Forestry Task analysis

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Learning monocular depth estimation infusing traditional stereo knowledge

Learning monocular depth estimation infusing traditional ste...

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Fabio Tosi Filippo Aleotti Matteo Poggi Stefano Mattoccia Department of Computer Science and Engineering (DISI) University of Bologna

ISBN: (纸本)9781728132945

Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications. Recent works proved that this task could be learned without direct supervision from ground truth labels leveraging image synthesis on sequences or stereo pairs. Focusing on this second case, in this paper we leverage stereo matching in order to improve monocular depth estimation. To this aim we propose monoResMatch, a novel deep architecture designed to infer depth from a single input image by synthesizing features from a different point of view, horizontally aligned with the input image, performing stereo matching between the two cues. In contrast to previous works sharing this rationale, our network is the first trained end-to-end from scratch. Moreover, we show how obtaining proxy ground truth annotation through traditional stereo algorithms, such as Semi-Global Matching, enables more accurate monocular depth estimation still countering the need for expensive depth labels by keeping a self-supervised approach. Exhaustive experimental results prove how the synergy between i) the proposed monoResMatch architecture and ii) proxy-supervision attains state-of-the-art for self-supervised monocular depth estimation. The code is publicly available at .

关键词： Stereo pairs Image synthesis ground truth Imagery (Psychotherapy) Image images Synergism

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SenSquare: a Mobile Crowdsensing Architecture for Smart Cities

SenSquare: a Mobile Crowdsensing Architecture for Smart Citi...

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World Forum on Internet of Things

作者： Federico Montori Luca Bedogni Alain Di Chiappari Luciano Bononi Department of Computer Science and Engineering (DISI) University of Bologna Italy

ISBN: (纸本)9781509041312

In the recent years the Smart City paradigm has gained interest worldwide. Services are built on top of data sensed in the city and then analyzed in order to enhance people's quality of life. Nowadays users are also able to participate in such a data gathering, mostly thanks to a reduction in the cost of the sensing devices. Moreover, smartphones encompass many useful sensors and can be leveraged to obtain data by end users on the move, within the scope of mobile crowdsensing. In this paper, we propose SenSquare, a mobile crowdsensing architecture for Smart Cities, built to embrace both data availability and devices heterogeneity. SenSquare also offers the possibility for stakeholders to reward users sharing their data. Finally, we compare our proposal against a non-smart ideal architecture, showing the benefits and the advantages of a smart architecture such as SenSquare.

关键词： Architecture Smart Cities

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On the integration of heterogeneous data sources for the collaborative Internet of Things

On the integration of heterogeneous data sources for the col...

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IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Federico Montori Luca Bedogni Luciano Bononi Department of Computer Science and Engineering (DISI) University of Bologna Italy

ISBN: (纸本)9781509011322

The Internet of things is foreseen as one of the next imminent Internet revolutions, as many devices will seamlessly communicate together to provide new and exciting services to the end users. One of the challenges that the IoT has to face is about both the heterogeneity of the data available and the heterogeneity of the communication. In this paper we focus on the former, by presenting an architecture able to integrate data coming from different sources, including custom made deployments and government data. New services can be deployed directly by the end users, using reliable or unreliable data sources, and new processed data can be gathered by these services and used by others.

关键词： Internet of Things collaborative filtering data integration distributed databases

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GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction

GO-SLAM: Global Optimization for Consistent 3D Instant Recon...

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International Conference on computer Vision (ICCV)

作者： Youmin Zhang Fabio Tosi Stefano Mattoccia Matteo Poggi Department of Computer Science and Engineering (DISI) University of Bologna Italy

Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in camera tracking and distortion in the reconstruction. Purposely, we present GO-SLAM, a deep-learning-based dense visual SLAM framework globally optimizing poses and 3D reconstruction in real-time. Robust pose estimation is at its core, supported by efficient loop closing and online full bundle adjustment, which optimize per frame by utilizing the learned global geometry of the complete history of input frames. Simultaneously, we update the implicit and continuous surface representation on-the-fly to ensure global consistency of 3D reconstruction. Results on various synthetic and real-world datasets demonstrate that GO-SLAM outperforms state-of-the-art approaches at tracking robustness and reconstruction accuracy. Furthermore, GO-SLAM is versatile and can run with monocular, stereo, and RGB-D input.

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