Готові списки джерел за темами / Road scene understanding

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Добірка наукової літератури з теми "Road scene understanding"

Автор: Grafiati

Опубліковано: 10 березня 2023

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Статті в журналах з теми "Road scene understanding"

Zhou, Wujie, Sijia Lv, Qiuping Jiang, and Lu Yu. "Deep Road Scene Understanding." IEEE Signal Processing Letters 26, no. 4 (April 2019): 587–91. http://dx.doi.org/10.1109/lsp.2019.2896793.

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Huang, Wenqi, Fuzheng Zhang, Aidong Xu, Huajun Chen, and Peng Li. "Fusion-based holistic road scene understanding." Journal of Engineering 2018, no. 16 (November 1, 2018): 1623–28. http://dx.doi.org/10.1049/joe.2018.8319.

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Wang, Chao, Huan Wang, Rui Li Wang, and Chun Xia Zhao. "Robust Zebra-Crossing Detection for Autonomous Land Vehicles and Driving Assistance Systems." Applied Mechanics and Materials 556-562 (May 2014): 2732–39. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2732.

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Road scene understanding is critical for driving assistance systems and autonomous land vehicles. The main function of road scene understanding is robustly detecting useful visual objects existing in a road scene. A zebra crossing is a typical pedestrian crossing used in many countries around the world. When detecting a zebra crossing, an autonomous lane vehicle is normally required to automatically slow down its speed and to trigger a path-planning strategy for passing the zebra crossing. Also, most of driving assistance systems can send an early-warning signal to remind drivers to be more careful. This paper proposes a robust zebra-crossing detection algorithm for autonomous land vehicles and driving assistance systems. Firstly, an inverse perspective map is generated by utilizing camera calibration parameters to obtain a bird-eye view road image. Secondly, a course-to-fine detection process is applied to obtain a candidate zebra-crossing region and finally a true zebra-crossing region is recognized by combining appearance and shape features. Experiments on several kinds of real road videos which also include several challenge scenes demonstrate the effectiveness and efficiency of the proposed method.

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Liu, Huajun, Cailing Wang, and Jingyu Yang. "Vanishing points estimation and road scene understanding based on Bayesian posterior probability." Industrial Robot: An International Journal 43, no. 1 (January 18, 2016): 12–21. http://dx.doi.org/10.1108/ir-05-2015-0095.

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Purpose – This paper aims to present a novel scheme of multiple vanishing points (VPs) estimation and corresponding lanes identification. Design/methodology/approach – The scheme proposed here includes two main stages: VPs estimation and lane identification. VPs estimation based on vanishing direction hypothesis and Bayesian posterior probability estimation in the image Hough space is a foremost contribution, and then VPs are estimated through an optimal objective function. In lane identification stage, the selected linear samples supervised by estimated VPs are clustered based on the gradient direction of linear features to separate lanes, and finally all the lanes are identified through an identification function. Findings – The scheme and algorithms are tested on real data sets collected from an intelligent vehicle. It is more efficient and more accurate than recent similar methods for structured road, and especially multiple VPs identification and estimation of branch road can be achieved and lanes of branch road can be identified for complex scenarios based on Bayesian posterior probability verification framework. Experimental results demonstrate VPs, and lanes are practical for challenging structured and semi-structured complex road scenarios. Originality/value – A Bayesian posterior probability verification framework is proposed to estimate multiple VPs and corresponding lanes for road scene understanding of structured or semi-structured road monocular images on intelligent vehicles.

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Yasrab, Robail. "ECRU: An Encoder-Decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding." Journal of Imaging 4, no. 10 (October 8, 2018): 116. http://dx.doi.org/10.3390/jimaging4100116.

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This research presents the idea of a novel fully-Convolutional Neural Network (CNN)-based model for probabilistic pixel-wise segmentation, titled Encoder-decoder-based CNN for Road-Scene Understanding (ECRU). Lately, scene understanding has become an evolving research area, and semantic segmentation is the most recent method for visual recognition. Among vision-based smart systems, the driving assistance system turns out to be a much preferred research topic. The proposed model is an encoder-decoder that performs pixel-wise class predictions. The encoder network is composed of a VGG-19 layer model, while the decoder network uses 16 upsampling and deconvolution units. The encoder of the network has a very flexible architecture that can be altered and trained for any size and resolution of images. The decoder network upsamples and maps the low-resolution encoder’s features. Consequently, there is a substantial reduction in the trainable parameters, as the network recycles the encoder’s pooling indices for pixel-wise classification and segmentation. The proposed model is intended to offer a simplified CNN model with less overhead and higher performance. The network is trained and tested on the famous road scenes dataset CamVid and offers outstanding outcomes in comparison to similar early approaches like FCN and VGG16 in terms of performance vs. trainable parameters.

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Topfer, Daniel, Jens Spehr, Jan Effertz, and Christoph Stiller. "Efficient Road Scene Understanding for Intelligent Vehicles Using Compositional Hierarchical Models." IEEE Transactions on Intelligent Transportation Systems 16, no. 1 (February 2015): 441–51. http://dx.doi.org/10.1109/tits.2014.2354243.

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Qin, Yuting, Yuren Chen, and Kunhui Lin. "Quantifying the Effects of Visual Road Information on Drivers’ Speed Choices to Promote Self-Explaining Roads." International Journal of Environmental Research and Public Health 17, no. 7 (April 3, 2020): 2437. http://dx.doi.org/10.3390/ijerph17072437.

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Анотація:

Roads should deliver appropriate information to drivers and thus induce safer driving behavior. This concept is also known as “self-explaining roads” (SERs). Previous studies have demonstrated that understanding how road characteristics affect drivers’ speed choices is the key to SERs. Thus, in order to reduce traffic casualties via engineering methods, this study aimed to establish a speed decision model based on visual road information and to propose an innovative method of SER design. It was assumed that driving speed is determined by road geometry and modified by the environment. Lane fitting and image semantic segmentation techniques were used to extract road features. Field experiments were conducted in Tibet, China, and 1375 typical road scenarios were picked out. By controlling variables, the driving speed stimulated by each piece of information was evaluated. Prediction models for geometry-determined speed and environment-modified speed were built using the random forest algorithm and convolutional neural network. Results showed that the curvature of the right boundary in “near scene” and “middle scene”, and the density of roadside greenery and residences play an important role in regulating driving speed. The findings of this research could provide qualitative and quantitative suggestions for the optimization of road design that would guide drivers to choose more reasonable driving speeds.

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Jeong, Jinhan, Yook Hyun Yoon, and Jahng Hyon Park. "Reliable Road Scene Interpretation Based on ITOM with the Integrated Fusion of Vehicle and Lane Tracker in Dense Traffic Situation." Sensors 20, no. 9 (April 26, 2020): 2457. http://dx.doi.org/10.3390/s20092457.

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Lane detection and tracking in a complex road environment is one of the most important research areas in highly automated driving systems. Studies on lane detection cover a variety of difficulties, such as shadowy situations, dimmed lane painting, and obstacles that prohibit lane feature detection. There are several hard cases in which lane candidate features are not easily extracted from image frames captured by a driving vehicle. We have carefully selected typical scenarios in which the extraction of lane candidate features can be easily corrupted by road vehicles and road markers that lead to degradations in the understanding of road scenes, resulting in difficult decision making. We have introduced two main contributions to the interpretation of road scenes in dense traffic environments. First, to obtain robust road scene understanding, we have designed a novel framework combining a lane tracker method integrated with a camera and a radar forward vehicle tracker system, which is especially useful in dense traffic situations. We have introduced an image template occupancy matching method with the integrated vehicle tracker that makes it possible to avoid extracting irrelevant lane features caused by forward target vehicles and road markers. Second, we present a robust multi-lane detection by a tracking algorithm that incudes adjacent lanes as well as ego lanes. We verify a comprehensive experimental evaluation with a real dataset comprised of problematic road scenarios. Experimental result shows that the proposed method is very reliable for multi-lane detection at the presented difficult situations.

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Sun, Jee-Young, Seung-Won Jung, and Sung-Jea Ko. "Lightweight Prediction and Boundary Attention-Based Semantic Segmentation for Road Scene Understanding." IEEE Access 8 (2020): 108449–60. http://dx.doi.org/10.1109/access.2020.3001679.

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Deng, Yanzi, Zhaoyang Lu, and Jing Li. "Coarse-to-fine road scene segmentation via hierarchical graphical models." International Journal of Advanced Robotic Systems 16, no. 2 (March 1, 2019): 172988141983116. http://dx.doi.org/10.1177/1729881419831163.

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The road scene segmentation is an important problem which is helpful for a higher level of the scene understanding. This article presents a novel approach for image semantic segmentation of road scenes via a hierarchical graph-based inference. A deep encoder–decoder network is first applied for a fast pixel-wise classification. Then, hierarchical graph-based inference is performed to get an accurate segmentation result. In the inference process, all the object classes are grouped into fewer categories which contains at least one class. The category labels are assigned to image superpixels using Markov random field model. For each category, a pixel-level labeling based on fully connected conditional random fields is performed to divide image into different classes. After the inference for all categories, the results are integrated together to get the final segmentation. In additional to low-level affinity functions, the feature maps from network are integrated in pairwise potentials of the graphical models. This hierarchical inference scheme can alleviate the confusion of classes belonging to different categories. It performs well for small objects without adding more computational burden. Both qualitative and quantitative assessments are adopted to evaluate the proposed method. The results on benchmark data sets prove the effectiveness of the proposed hierarchical scheme, and the performance is competitive with the state-of-the-art methods.

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Більше джерел

Дисертації з теми "Road scene understanding"

Habibi, Aghdam Hamed. "Understanding Road Scenes using Deep Neural Networks." Doctoral thesis, Universitat Rovira i Virgili, 2018. http://hdl.handle.net/10803/461607.

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La comprensió de les escenes de la carretera és fonamental per als automòbils autònoms. Això requereix segmentar escenes de carreteres en regions semànticament significatives i reconèixer objectes en una escena. Tot i que objectes com ara cotxes i vianants han de segmentar-se amb precisió, és possible que no sigui necessari detectar i localitzar aquests objectes en una escena. Tanmateix, detectar i classificar objectes com ara els senyals de trànsit és fonamental per ajustar-se a les regles del camí. En aquesta tesi, primer proposem un mètode per classificar senyals de trànsit amb atributs visuals i xarxes bayesianes. A continuació, proposem dues xarxes neuronals per a aquest propòsit i desenvolupem un nou mètode per crear un conjunt de models. A continuació, estudiem la sensibilitat de les xarxes neuronals contra mostres adversàries i proposem dues xarxes de denoising que s'adjunten a les xarxes de classificació per augmentar la seva estabilitat contra el soroll. A la segona part de la tesi, primer proposem una xarxa per detectar senyals de trànsit en imatges d'alta resolució en temps real i mostrar com implementar la tècnica de la finestra d'escaneig dins de la nostra xarxa utilitzant convolucions dilatades. A continuació, formulem el problema de detecció com a problema de segmentació i proposem una xarxa totalment convolucional per detectar senyals de trànsit. ? Finalment, proposem una nova xarxa totalment convolucional composta de mòduls de foc, connexions de derivació i convolucions consecutives dilatades? En l'última part de la tesi per a escenes de camins segmentinc en regions semànticament significatives i demostrar que és més accentuat i computacionalment més eficient en comparació amb xarxes similars
Comprender las escenas de la carretera es crucial para los automóviles autónomos. Esto requiere segmentar escenas de carretera en regiones semánticamente significativas y reconocer objetos en una escena. Mientras que los objetos tales como coches y peatones tienen que segmentarse con precisión, puede que no sea necesario detectar y localizar estos objetos en una escena. Sin embargo, la detección y clasificación de objetos tales como señales de tráfico es esencial para ajustarse a las reglas de la carretera. En esta tesis, proponemos un método para la clasificación de señales de tráfico utilizando atributos visuales y redes bayesianas. A continuación, proponemos dos redes neuronales para este fin y desarrollar un nuevo método para crear un conjunto de modelos. A continuación, se estudia la sensibilidad de las redes neuronales frente a las muestras adversarias y se proponen dos redes destructoras que se unen a las redes de clasificación para aumentar su estabilidad frente al ruido. En la segunda parte de la tesis, proponemos una red para detectar señales de tráfico en imágenes de alta resolución en tiempo real y mostrar cómo implementar la técnica de ventana de escaneo dentro de nuestra red usando circunvoluciones dilatadas. A continuación, formulamos el problema de detección como un problema de segmentación y proponemos una red completamente convolucional para detectar señales de tráfico. Finalmente, proponemos una nueva red totalmente convolucional compuesta de módulos de fuego, conexiones de bypass y circunvoluciones consecutivas dilatadas en la última parte de la tesis para escenarios de carretera segmentinc en regiones semánticamente significativas y muestran que es más accuarate y computacionalmente más eficiente en comparación con redes similares
Understanding road scenes is crucial for autonomous cars. This requires segmenting road scenes into semantically meaningful regions and recognizing objects in a scene. While objects such as cars and pedestrians has to be segmented accurately, it might not be necessary to detect and locate these objects in a scene. However, detecting and classifying objects such as traffic signs is essential for conforming to road rules. In this thesis, we first propose a method for classifying traffic signs using visual attributes and Bayesian networks. Then, we propose two neural network for this purpose and develop a new method for creating an ensemble of models. Next, we study sensitivity of neural networks against adversarial samples and propose two denoising networks that are attached to the classification networks to increase their stability against noise. In the second part of the thesis, we first propose a network to detect traffic signs in high-resolution images in real-time and show how to implement the scanning window technique within our network using dilated convolutions. Then, we formulate the detection problem as a segmentation problem and propose a fully convolutional network for detecting traffic signs. Finally, we propose a new fully convolutional network composed of fire modules, bypass connections and consecutive dilated convolutions in the last part of the thesis for segmenting road scenes into semantically meaningful regions and show that it is more accurate and computationally more efficient compared to similar networks.

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Lee, Jong Ho. "Understanding the Visual Appearance of Road Scenes Using a Monocular Camera." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/795.

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Over the past several decades, research efforts in the development of self-driving vehicles have drastically improved accompanying technologies. Since the challenges held by Defense Advanced Research Projects Agency, the autonomous driving industry has increased significantly, and almost all the automotive companies have started to develop the technologies to deploy autonomous driving vehicles in the real world. Even though a lot of companies have been making efforts to achieve fully automated vehicles, the current technologies are not mature enough to be deployed in the real world yet, because self-driving vehicles need to respond to uncontrolled environments, such as moving objects, pedestrians, traffic lights, and unexpected work-zones. Among these uncontrolled environments, this thesis focuses on understanding road information and estimating states of traffic lights. Given that all of the traffic control devices are regularized in colors, color is one of the most significant features to be recognized. In order to accomplish such necessary a vision task, self-driving vehicles must incorporate cameras. Despite the fact that traffic control devices have their own regularized color and cameras can see those devices, they are still difficult to detect and recognize by autonomous vehicles. One of the biggest problems is that the color of those devices can be captured differently based on illumination. In this thesis, we investigate the problem of recognizing static objects using a monocular camera to assist self-driving vehicles in perceiving traffic control devices. The perception system, specifically a camera, should recognize the objects robustly regardless of the environment. Throughout this thesis, we exploit different color spaces and apply machine learning to reduce color variance. Also, we develop algorithms which compensate for illumination changes by considering the Sun position, to further improve the road sign recognition. Furthermore, we improve a traffic light state estimation which performs robustly under various illumination conditions. We deploy and demonstrate all of the algorithms in an autonomous vehicle.

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Wang, Fan. "How polarimetry may contribute to understand reflective road scenes : theory and applications." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0003/document.

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Les systèmes d'aide à la conduite (ADAS) visent à automatiser/ adapter/ améliorer les systèmes de transport pour une meilleure sécurité et une conduite plus sûre. Plusieurs thématiques de recherche traitent des problématiques autour des ADAS, à savoir la détection des obstacles, la reconnaissance de formes, la compréhension des images, la stéréovision, etc. La présence des réflexions spéculaires limite l'efficacité et la précision de ces algorithmes. Elles masquent les textures de l'image originale et contribuent à la perte de l'information utile. La polarisation de la lumière traduit implicitement l'information attachée à l'objet, telle que la direction de la surface, la nature de la matière, sa rugosité etc. Dans le contexte des ADAS, l'imagerie polarimétrique pourrait être utilisée efficacement pour éliminer les réflexions parasites des images et analyser d'une manière précise les scènes routières. Dans un premier temps, nous proposons dans cette thèse de supprimer les réflexions spéculaires des images via la polarisation en appliquant une minimisation d'énergie globale. L'information polarimétrique fournit une contrainte qui réduit les distorsions couleurs et produit une image diffuse beaucoup plus améliorée. Nous avons ensuite proposé d'utiliser les images de polarisation comme une caractéristique vu que dans les scènes routières, les hautes réflexions proviennent particulièrement de certains objets telles que les voitures. Les attributs polarimétriques sont utilisés pour la compréhension de la scène et la détection des voitures. Les résultats expérimentaux montrent que, une fois correctement fusionnés avec les caractéristiques couleur, les attributs polarimétriques offrent une information complémentaire qui améliore considérablement les résultats de la détection.Nous avons enfin testé l'imagerie de polarisation pour l'estimation de la carte de disparité. Une méthode d'appariement est proposée et validée d'abord sur une base de données couleur. Ensuite, Une règle de fusion est proposée afin d'utiliser l'imagerie polarimétrique comme une contrainte pour le calcul de la carte de disparité. A partir des différents résultats obtenus, nous avons prouvé le potentiel et la faisabilité d'appliquer l'imagerie de polarisation dans différentes applications liées aux systèmes d’aide à la conduite
Advance Driver Assistance Systems (ADAS) aim to automate/adapt/enhance trans-portation systems for safety and better driving. Various research topics are emerged to focus around the ADAS, including the object detection and recognition, image understanding, disparity map estimation etc. The presence of the specular highlights restricts the accuracy of such algorithms, since it covers the original image texture and leads to the lost of information. Light polarization implicitly encodes the object related information, such as the surface direction, material nature, roughness etc. Under the context of ADAS, we are inspired to further inspect the usage of polarization imaging to remove image highlights and analyze the road scenes.We firstly propose in this thesis to remove the image specularity through polarization by applying a global energy minimization. Polarization information provides a color constraint that reduces the color distortion of the results. The global smoothness assumption further integrates the long range information in the image and produces an improved diffuse image.We secondly propose to use polarization images as a new feature, since for the road scenes, the high reflection appears only upon certain objects such as cars. Polarization features are applied in image understanding and car detection in two different ways. The experimental results show that, once properly fused with rgb-based features, the complementary information provided by the polarization images improve the algorithm accuracy. We finally test the polarization imaging for depth estimation. A post-aggregation stereo matching method is firstly proposed and validated on a color database. A fusion rule is then proposed to use the polarization imaging as a constraint to the disparity map estimation. From these applications, we proved the potential and the feasibility to apply polariza-tion imaging in outdoor tasks for ADAS

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Kung, Wen Yao, and 龔芠瑤. "Road Scene Understanding with Semantic Segmentation and Object Hazard Level Prediction." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/60494150880821921387.

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碩士
國立清華大學
資訊工程學系
104
We introduce a method for understanding road scenes and simultaneously predicting the hazard levels of three categories of objects in road scene images by using a fully convolutional network (FCN) architecture. In our approach, with a single input image, the multi-task model produces a _ne segmentation result and a prediction of hazard levels in a form of heatmap. The model can be divided into three parts: shared net, segmentation net, and hazard level net. The shared net and segmentation net use the encoder-decoder architecture provided by Badrinarayanan et al . [2]. The hazard level net is a fully convolution network estimating hazard level of a segment with a coarse segmentation result. We also provide a dataset with the object segmentation ground truth and the hazard levels for training and evaluating the proposed deep networks. To prove that our network can learn highly semantic attributes of objects, we use two measurements to evaluate the performance of our method, and compare our method with a saliency-based method to show the difference between predicting hazard levels and estimating human eyes fixations.

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Schoen, Fabio. "Deep learning methods for safety-critical driving events analysis." Doctoral thesis, 2022. http://hdl.handle.net/2158/1260238.

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In this thesis, we propose to study the data of crash and near-crash events, collectively called safety-critical driving events. Such data include a footage of the event, acquired from a camera mounted inside the vehicle, and the data from a GPS/IMU module, i.e., speed, acceleration and angular velocity. First, we introduce a novel problem, that we call unsafe maneuver classification, that aims at classifying safety-critical driving events based on the maneuver that leads to the unsafe situation and we propose a two-stream neural architecture based on Convolutional Neural Networks that performs sensor fusion and address the classification task. Then, we propose to integrate the output of an object detector in the classification task, to provide the network explicit knowledge of the entities in the scene. We design a specific architecture that leverages a tracking algorithm to extract information of a single real-world object over time, and then uses attention to ground the prediction on a single (or a few) objects, i.e., the dangerous or in danger ones, leveraging a solution that we called Spatio-Temporal Attention Selector (STAS). Finally, we propose to address video captioning of safety-critical events, with the goal of providing a description of the dangerous situation in a human-understandable form.

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Hummel, Britta [Verfasser]. "Description logic for scene understanding at the example of urban road intersections / von Britta Hummel." 2009. http://d-nb.info/1000324818/34.

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Книги з теми "Road scene understanding"

Voparil, Chris. Reconstructing Pragmatism. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197605721.001.0001.

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The figure of Richard Rorty stands in complex relation to the tradition of American pragmatism. On the one hand, his intellectual creativity, lively prose, and bridge-building fueled the contemporary resurgence of pragmatism. On the other, his polemical claims and selective interpretations function as a negative, fixed pole against which thinkers of all stripes define themselves. Virtually all pragmatists on the contemporary scene, whether classical or “new,” Deweyan, Jamesian, or Peircean, use Rorty as a foil to justify their positions. The resulting divisions and internecine quarrels threaten to thwart and fragment the tradition’s creative potential. More caricatured than understood, the specter of Rorty is blocking the road of inquiry and future development of pragmatism. Reconstructing Pragmatism moves beyond the Rortyan impasse by providing what has been missing for decades: a constructive, nonpolemical account of Rorty’s relation to classical pragmatism. The first book-length treatment of Rorty’s intellectual debt to the early pragmatists, it establishes his selective appropriations not as misunderstandings or distortions but as a sustained, intentional effort to reconstruct their thinking. Featuring chapters devoted to five key pragmatist thinkers—Charles Sanders Peirce, William James, John Dewey, Josiah Royce, and Jane Addams—the book draws on archival sources and the full scope of Rorty’s writings to challenge prevailing misconceptions and caricatures. By illuminating the critical resources, still largely untapped, that Rorty offers for articulating classical pragmatism’s ongoing relevance, the book reveals limitations in received images of the classical pragmatists and opens up new modes of understanding pragmatism and why it matters today.

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Biel Portero, Israel, Andrea Carolina Casanova Mejía, Amanda Janneth Riascos Mora, Alba Lucy Ortega Salas, Luis Andrés Salas Zambrano, Franco Andrés Montenegro Coral, Julie Andrea Benavides Melo, et al. Challenges and alternatives towards peacebuilding. Edited by Ángela Marcela Castillo Burbano and Claudia Andrea Guerrero Martínez. Ediciones Universidad Cooperativa de Colombia, 2020. http://dx.doi.org/10.16925/9789587602388.

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Rural development and peacebuilding in Colombia have been highly prioritized by higher education institutions since the signing of the Peace Agreement between the National Government and the FARC-EP. This has resulted in the need to further analyze rural strategies that contribute towards a better life for the population of territories where armed conflict is coming to an end, whilst understanding the pressing uncertainty that this process implies; on the one hand, for the urgency of generating rapid and concrete responses to social justice and equity, and on the other, because fulfilling the agreement guarantees scenarios of non-repetition of the war in the country. These were some of the reflections that motivated the research project “Rural development alternatives for peacebuilding: educational strategies to strengthen the ability of producers and young people that contribute to the coffee production chain in the municipalities of Leiva, Policarpa and Los Andes of the department of Narino, with international impact in the province of Carchi-Ecuador”. This work is presented as an investigative result that contains the analysis of theoretical and territorial Dynamic contributions regarding the construction of peace, education and the economy for rural development. The book is made up of three parts: Part 1 gathers sociological, legal and demographic works on the challenges of peacebuilding with the national and departmental context of Narino, and looks at human rights from the perspective of population health and quality of life. Part 2 presents texts on the dynamics of rural education in Colombia; national challenges and lessons learned based on case studies of specific forms of education. Part 3 presents economic analyses regarding the models that are behind the conception of rural development and the productive and institutional dynamics of the local sphere for the generation of employment and income. All three parts are relevant at both the national level and also the more specific area of the department of Narino and within this, the Cordillera region. This area, historically affected by the armed conflict, despite experiencing continuing uncertainty regarding the resurgence of violence and the increase in illegal crops, has also reignited hope with regards to finding solutions to the problems seen in the countryside; through educational, community and productive experiments. Although there are contradictory dynamics, the authors agree that the rural territory is a scene of permanent and collective construction, mediated by constant social struggles and power disputes with the State. It is therefore necessary to rethink the strategies for implementing the Peace Agreement in this region, with participatory scenarios being provided to include the rationale specific to rurality, such as: justice and reconciliation, social pedagogy, pertinence of study and student retention rates, social and solidarity economy, productive associativity, demographic conditions and health; including the physical, mental and social wellbeing of rural workers. With this work, we hope to reflect collectively with academics and human rights activists, spurring an increase in studies of rural areas and those analyses of community and innovative strategies that reinforce the road towards the construction of a lasting peace with social justice in Colombia.

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Частини книг з теми "Road scene understanding"

Holder, Christopher J., and Toby P. Breckon. "Encoding Stereoscopic Depth Features for Scene Understanding in off-Road Environments." In Lecture Notes in Computer Science, 427–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_48.

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Kembhavi, Aniruddha, Tom Yeh, and Larry S. Davis. "Why Did the Person Cross the Road (There)? Scene Understanding Using Probabilistic Logic Models and Common Sense Reasoning." In Computer Vision – ECCV 2010, 693–706. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15552-9_50.

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Alvarez, Jose M., Felipe Lumbreras, Antonio M. Lopez, and Theo Gevers. "Understanding Road Scenes Using Visual Cues and GPS Information." In Computer Vision – ECCV 2012. Workshops and Demonstrations, 635–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33885-4_70.

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Oeljeklaus, Malte. "5 Global Road Topology from Scene Context Recognition." In An Integrated Approach for Traffic Scene Understanding from Monocular Cameras, 38–49. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783186815125-38.

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Oeljeklaus, Malte. "7 Road Users from Bounding Box Detection." In An Integrated Approach for Traffic Scene Understanding from Monocular Cameras, 64–83. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783186815125-64.

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Lapierre, Isabelle, and Claude Laurgeau. "A Road Scene Understanding System based on a Blackboard Architecture." In Advances In Structural And Syntactic Pattern Recognition, 571–85. WORLD SCIENTIFIC, 1993. http://dx.doi.org/10.1142/9789812797919_0048.

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Oeljeklaus, Malte. "6 Drivable Road Area from Semantic Image Segmentation." In An Integrated Approach for Traffic Scene Understanding from Monocular Cameras, 50–63. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783186815125-50.

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Winter, Tim. "The Routes of Civilization." In The Silk Road, 23–33. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780197605059.003.0002.

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Анотація:

The first part of the book examines the processes and events by which the Silk Road gains its key attributes as a narrative of geocultural history in the five decades leading up to World War II. This first chapter begins by outlining the connections forged in nineteenth-century Europe between the nation, material culture, and ideas of civilization and East and West. An explanation of the geopolitical contexts within which histories of the Middle East are written by European scholars sets the scene for understanding the factors that led to Ferdinand von Richthofen describing historical trade routes in Central Asia as a road of silk. The circumstances of his trip meant that he combined surveys for a possible rail route to Europe with his broader interests in geography and history. This is fundamentally important to how the Silk Road narrative forms decades later.

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Тези доповідей конференцій з теми "Road scene understanding"

Dhiman, Vikas, Quoc-Huy Tran, Jason J. Corso, and Manmohan Chandraker. "A Continuous Occlusion Model for Road Scene Understanding." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.469.

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Sun, Yuan, Hongbo Lu, and Zhimin Zhang. "RvGIST: A Holistic Road Feature for Real-Time Road-Scene Understanding." In 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2013. http://dx.doi.org/10.1109/snpd.2013.86.

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Venkateshkumar, Suhas Kashetty, Muralikrishna Sridhar, and Patrick Ott. "Latent Hierarchical Part Based Models for Road Scene Understanding." In 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). IEEE, 2015. http://dx.doi.org/10.1109/iccvw.2015.25.

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Tsukada, A., M. Ogawa, and F. Galpin. "Road structure based scene understanding for intelligent vehicle systems." In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iros.2010.5653532.

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Sturgess, Paul, Karteek Alahari, Lubor Ladicky, and Philip H. S. Torr. "Combining Appearance and Structure from Motion Features for Road Scene Understanding." In British Machine Vision Conference 2009. British Machine Vision Association, 2009. http://dx.doi.org/10.5244/c.23.62.

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Murthy, J. Krishna, G. V. Sai Krishna, Falak Chhaya, and K. Madhava Krishna. "Reconstructing vehicles from a single image: Shape priors for road scene understanding." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989089.

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Duong, Tin Trung, Huy-Hung Nguyen, and Jae Wook Jeon. "TSS-Net: Time-based Semantic Segmentation Neural Network for Road Scene Understanding." In 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2021. http://dx.doi.org/10.1109/imcom51814.2021.9377401.

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Topfer, Daniel, Jens Spehr, Jan Effertz, and Christoph Stiller. "Efficient scene understanding for intelligent vehicles using a part-based road representation." In 2013 16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/itsc.2013.6728212.

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Yang, Mingdong, Hongkun Zhou, Wenjun Huo, and Guanglu Ren. "JDSNet: Joint Detection and Segmentation Network for Real-Time Road Scene Understanding." In 2022 7th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2022. http://dx.doi.org/10.1109/icivc55077.2022.9886996.

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Nurhadiyatna, Adi, and Sven Loncaric. "Multistage Shallow Pyramid Parsing for Road Scene Understanding Based on Semantic Segmentation." In 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2019. http://dx.doi.org/10.1109/ispa.2019.8868554.

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