Dr. Marcos Nieto Profile

Dr. Marcos Nieto

Researcher at Vicomtech

SPIE Involvement:

Author

Area of Expertise:

Computer vision , Probability theory , Image processing , Object tracking , Programming

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Marcos Nieto received the M.S. and Ph.D. degree in electrical engineering from ETSIT of the Universidad Politécnica de Madrid (UPM), Spain, in 2005 and 2010, respectively.
From 2005 to 2010 he worked as Researcher within the Grupo de Tratamiento de Imágenes in the UPM, in the field of H.264/AVC video codecs and computer vision applied to advanced driver assistance systems. Since 2010 he works as a Researcher on computer vision and machine learning techniques of the Intelligent Transport Systems and Engineering Area of Vicomtech. His actual research interests include the use of optimization methods for probabilistic models in computer vision.

Proceedings Article | 7 June 2023 Paper

Method for the automatic measurement of camera-calibration quality in a surround-view system

Martí Sánchez, Jon Ander Iñiguez de Gordoa, Marcos Nieto, Pablo Carballeira

Proceedings Volume 12701, 127011F (2023) https://doi.org/10.1117/12.2679298

KEYWORDS: Cameras, Calibration, Histograms of oriented gradient, Camera calibration, Feature extraction, Visualization

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Over the last decade, the automotive industry has introduced advanced driving assistance systems (ADAS) and automated driving (AD) features into roads to reduce fatality rates. One of these ADAS is the surround-view system, which provides an orthographic view of the vehicle by using at least four fish-eye lens cameras embedded in it. Small bumps or temperature changes may modify these cameras' relative poses leading to some geometrical mismatches between views in the top-view projection plane. In addition, terrain irregularities may misalign the orthographic view with the ground plane surface. Both problems can be solved by reestimating the relative poses of the cameras with respect to a single common point in the vehicle. This procedure, also known as recalibration, is offline performed in technical garages, or by online calibration mechanisms on engine start. However, it is a slow and cumbersome process. Research to date studies how to optimally recalibrate these cameras in an online manner, neglecting the practical aspects of when this procedure should be undertaken. Therefore, depending on the functionalities for which the embedded cameras are required, a compromise between using out-of-calibration cameras and the consequences derived from the recalibration process must be considered. This would prevent reestimating the cameras’ relative poses in situations where misalignment between adjacent cameras may not be noticeable. For this reason, a novel approach that measures the degree of calibration between cameras embedded in a vehicle is proposed. This method extracts relevant features from the predefined regions of interest of each camera by using the histogram of oriented gradients (HOG) descriptor. Then, features that belong to adjacent cameras are compared by employing the cosine similarity metric. The proposed method is evaluated on the open-source AD research simulator CARLA providing detailed analysis to objectively highlight the usefulness of this method in studying the degree of calibration of a camera array in a surround-view system.

Proceedings Article | 7 June 2023 Paper

Automatic UAV-based airport pavement inspection using mixed real and virtual scenarios

Pablo Alonso, Jon Ander Iñiguez de Gordoa, Juan Diego Ortega, Sara García, Francisco Javier Iriarte, Marcos Nieto

Proceedings Volume 12701, 1270118 (2023) https://doi.org/10.1117/12.2679734

KEYWORDS: Artificial neural networks, Image segmentation, Data modeling, Inspection, Unmanned aerial vehicles, RGB color model, Visualization, Cameras

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Proceedings Article | 12 April 2021 Presentation + Paper

A real-time software framework for driver monitoring systems: software architecture and use cases

Juan Diego Ortega, Marcos Nieto, Paola Cañas, Oihana Otaegui, Luis Salgado

Proceedings Volume 11736, 117360E (2021) https://doi.org/10.1117/12.2588712

KEYWORDS: Software frameworks, Computer architecture, Switches, Safety, Roads, Rapid manufacturing, Network security, Machine vision, Human-machine interfaces, Data processing

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Proceedings Article | 4 May 2010 Paper

Real-time robust estimation of vanishing points through nonlinear optimization

Marcos Nieto, Luis Salgado

Proceedings Volume 7724, 772402 (2010) https://doi.org/10.1117/12.854592

KEYWORDS: Image segmentation, Error analysis, Optical spheres, Cameras, Calibration, Detection and tracking algorithms, Image processing, Video, Data modeling, Image processing algorithms and systems

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Vanishing points are elements of great interest in the computer vision field, since they are the main source of information about the geometry of the scene and the projection process associated to the camera. They have been studied and applied during decades for plane rectification, 3D reconstruction, and mainly auto-calibration tasks. Nevertheless, the literature lacks accurate online solutions for multiple vanishing point estimation. Most strategies focalize on the accuracy, using highly computational demanding iterative procedures. We propose a novel strategy for multiple vanishing point estimation that finds a trade-off between accuracy and efficiency, being able to operate in real time for video sequences. This strategy takes advantage of the temporal coherence of the images of the sequences to reduce the computational load of the processing algorithms while keeping a high level of accuracy due to an optimization process. The key element of the approach is a robust scheme based on the MLESAC algorithm, which is used in a similar way to the EM algorithm. This approach ensures robust and accurate estimations, since we use the MLESAC in combination with a novel error function, based on the angular error between the vanishing point and the image features. To increase the speed of the MLESAC algorithm, the selection of the minimal sample sets is substituted by a random sampling step that takes into account temporal information to provide better initializations. Besides, for the sake of flexibility, the proposed error function has been designed to work using as image features indiscriminately gradient-pixels or line segments. Hence, we increase the range of applications in which our approach can be used, according to the type of information that is available. The results show a real-time system that delivers real-time accurate estimations of multiple vanishing points for online processing, tested in moving camera video sequences of structured scenarios, both indoors and outdoors, such as rooms, corridors, facades, roads, etc.

Proceedings Article | 5 February 2009 Paper

Real-time vehicle detection and tracking based on perspective and non-perspective space cooperation

Jon Arróspide, Luis Salgado, Marcos Nieto, Fernando Jaureguizar

Proceedings Volume 7244, 72440H (2009) https://doi.org/10.1117/12.812253

KEYWORDS: Image segmentation, Image processing, Roads, Image enhancement, Video processing, Chemical elements, Cameras, Transform theory, Distortion, Detection and tracking algorithms

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