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	Author	Daniel Ponsa; Antonio Lopez
	Title	Vehicle Trajectory Estimation based on Monocular Vision			Type	Conference Article
	Year	2007	Publication	3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477	Abbreviated Journal
	Volume		Issue		Pages	587-594
	Keywords	vehicle detection
	Abstract
	Address	Girona (Spain)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ PoL2007a			Serial	785
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	Author	Daniel Ponsa; Antonio Lopez
	Title	Feature Selection Based on a New Formulation of the Minimal-Redundancy-Maximal-Relevance Criterion			Type	Conference Article
	Year	2007	Publication	3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477	Abbreviated Journal
	Volume		Issue		Pages	47-54
	Keywords
	Abstract
	Address	Girona (Spain)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ PoL2007b			Serial	787
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	Author	Daniel Ponsa; Antonio Lopez
	Title	Cascade of Classifiers for Vehicle Detection			Type	Conference Article
	Year	2007	Publication	Advanced Concepts for Intelligent Vision Systems, LNCS 4678, volume 1, pp. 980–989	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	vehicle detection
	Abstract
	Address	Delft (Netherlands)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ PoL2007c			Serial	935
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	Author	Daniel Ponsa; Antonio Lopez; Felipe Lumbreras; Joan Serrat; T. Graf
	Title	3D Vehicle Sensor based on Monocular Vision			Type	Miscellaneous
	Year	2005	Publication	Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1096–1101, ISBN:0–7803–9216–7	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Vienna (Austria)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ PLL2005			Serial	614
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	Author	Daniel Ponsa; Antonio Lopez; Joan Serrat; Felipe Lumbreras; T. Graf
	Title	Multiple Vehicle 3D Tracking Using an Unscented Kalman Filter			Type	Miscellaneous
	Year	2005	Publication	Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1108–1113, ISBN:0–7803–9216–7	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	vehicle detection
	Abstract
	Address	Vienna (Austria)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ PLS2005			Serial	615
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	Author	Daniel Ponsa; Joan Serrat; Antonio Lopez
	Title	On-board image-based vehicle detection and tracking			Type	Journal Article
	Year	2011	Publication	Transactions of the Institute of Measurement and Control	Abbreviated Journal	TIM
	Volume	33	Issue	7	Pages	783-805
	Keywords	vehicle detection
	Abstract	In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ PSL2011			Serial	1413
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	Author	Daniel Ponsa; Jordi Vitria
	Title	Mobile monitoring system using an agent-oriented approach			Type	Miscellaneous
	Year	1999	Publication	Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Bilbao
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS;OR;MV			Approved	no
	Call Number	ADAS @ adas @ DaV1999			Serial	21
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	Author	Daniel Ponsa; Robert Benavente; Felipe Lumbreras; Judit Martinez; Xavier Roca
	Title	Quality control of safety belts by machine vision inspection for real-time production			Type	Journal Article
	Year	2003	Publication	Optical Engineering (IF: 0.877)	Abbreviated Journal
	Volume	42	Issue	4	Pages	1114-1120
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher	SPIE	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS;ISE;CIC			Approved	no
	Call Number	ADAS @ adas @ PRL2003			Serial	399
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	Author	Daniel Ponsa; Xavier Roca
	Title	Multiple Model Approach to Deformable Shape Tracking			Type	Miscellaneous
	Year	2003	Publication	In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science 2652:782–792	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Springer-Verlag
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS;ISE			Approved	no
	Call Number	ADAS @ adas @ PoR2003			Serial	397
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	Author	Daniel Ponsa; Xavier Roca
	Title	Unsupervised Parameterisation of Gaussian Mixture Models			Type	Miscellaneous
	Year	2002	Publication		Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS;ISE			Approved	no
	Call Number	ADAS @ adas @ PoR2002c			Serial	313
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	Author	Daniel Ponsa; Xavier Roca
	Title	A Novel Approach to Generate Multiple Shape Models.			Type	Miscellaneous
	Year	2002	Publication	Articulated Motion and Deformable Objects, LNCS 2492: 80–91, Springer Verlag.	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS;ISE			Approved	no
	Call Number	ADAS @ adas @ PoR2002a			Serial	283
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	Author	Daniel Sanchez; J.C.Ortega; Miguel Angel Bautista
	Title	Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization			Type	Conference Article
	Year	2013	Publication	6th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	7887	Issue		Pages	50-58
	Keywords	Human Body Segmentation; Error-Correcting Output Codes; Cascade of Classifiers; Graph Cuts
	Abstract	Human body segmentation is a hard task because of the high variability in appearance produced by changes in the point of view, lighting conditions, and number of articulations of the human body. In this paper, we propose a two-stage approach for the segmentation of the human body. In a first step, a set of human limbs are described, normalized to be rotation invariant, and trained using cascade of classifiers to be split in a tree structure way. Once the tree structure is trained, it is included in a ternary Error-Correcting Output Codes (ECOC) framework. This first classification step is applied in a windowing way on a new test image, defining a body-like probability map, which is used as an initialization of a GMM color modelling and binary Graph Cuts optimization procedure. The proposed methodology is tested in a novel limb-labelled data set. Results show performance improvements of the novel approach in comparison to classical cascade of classifiers and human detector-based Graph Cuts segmentation approaches.
	Address	Madeira; Portugal; June 2013
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-38627-5	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	HUPBA			Approved	no
	Call Number	SOB2013			Serial	2250
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	Author	Daniel Sanchez; Meysam Madadi; Marc Oliu; Sergio Escalera
	Title	Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation			Type	Conference Article
	Year	2019	Publication	14th IEEE International Conference on Automatic Face and Gesture Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	While many individual tasks in the domain of human analysis have recently received an accuracy boost from deep learning approaches, multi-task learning has mostly been ignored due to a lack of data. New synthetic datasets are being released, filling this gap with synthetic generated data. In this work, we analyze four related human analysis tasks in still images in a multi-task scenario by leveraging such datasets. Specifically, we study the correlation of 2D/3D pose estimation, body part segmentation and full-body depth estimation. These tasks are learned via the well-known Stacked Hourglass module such that each of the task-specific streams shares information with the others. The main goal is to analyze how training together these four related tasks can benefit each individual task for a better generalization. Results on the newly released SURREAL dataset show that all four tasks benefit from the multi-task approach, but with different combinations of tasks: while combining all four tasks improves 2D pose estimation the most, 2D pose improves neither 3D pose nor full-body depth estimation. On the other hand 2D parts segmentation can benefit from 2D pose but not from 3D pose. In all cases, as expected, the maximum improvement is achieved on those human body parts that show more variability in terms of spatial distribution, appearance and shape, e.g. wrists and ankles.
	Address	Lille; France; May 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	FG
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ SMO2019			Serial	3326
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	Author	Daniel Sanchez; Miguel Angel Bautista; Sergio Escalera
	Title	HuPBA 8k+: Dataset and ECOC-GraphCut based Segmentation of Human Limbs			Type	Journal Article
	Year	2015	Publication	Neurocomputing	Abbreviated Journal	NEUCOM
	Volume	150	Issue	A	Pages	173–188
	Keywords	Human limb segmentation; ECOC; Graph-Cuts
	Abstract	Human multi-limb segmentation in RGB images has attracted a lot of interest in the research community because of the huge amount of possible applications in fields like Human-Computer Interaction, Surveillance, eHealth, or Gaming. Nevertheless, human multi-limb segmentation is a very hard task because of the changes in appearance produced by different points of view, clothing, lighting conditions, occlusions, and number of articulations of the human body. Furthermore, this huge pose variability makes the availability of large annotated datasets difficult. In this paper, we introduce the HuPBA8k+ dataset. The dataset contains more than 8000 labeled frames at pixel precision, including more than 120000 manually labeled samples of 14 different limbs. For completeness, the dataset is also labeled at frame-level with action annotations drawn from an 11 action dictionary which includes both single person actions and person-person interactive actions. Furthermore, we also propose a two-stage approach for the segmentation of human limbs. In a first stage, human limbs are trained using cascades of classifiers to be split in a tree-structure way, which is included in an Error-Correcting Output Codes (ECOC) framework to define a body-like probability map. This map is used to obtain a binary mask of the subject by means of GMM color modelling and GraphCuts theory. In a second stage, we embed a similar tree-structure in an ECOC framework to build a more accurate set of limb-like probability maps within the segmented user mask, that are fed to a multi-label GraphCut procedure to obtain final multi-limb segmentation. The methodology is tested on the novel HuPBA8k+ dataset, showing performance improvements in comparison to state-of-the-art approaches. In addition, a baseline of standard action recognition methods for the 11 actions categories of the novel dataset is also provided.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ SBE2015			Serial	2552
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	Author	Daniela Rato; Miguel Oliveira; Vitor Santos; Manuel Gomes; Angel Sappa
	Title	A sensor-to-pattern calibration framework for multi-modal industrial collaborative cells			Type	Journal Article
	Year	2022	Publication	Journal of Manufacturing Systems	Abbreviated Journal	JMANUFSYST
	Volume	64	Issue		Pages	497-507
	Keywords	Calibration; Collaborative cell; Multi-modal; Multi-sensor
	Abstract	Collaborative robotic industrial cells are workspaces where robots collaborate with human operators. In this context, safety is paramount, and for that a complete perception of the space where the collaborative robot is inserted is necessary. To ensure this, collaborative cells are equipped with a large set of sensors of multiple modalities, covering the entire work volume. However, the fusion of information from all these sensors requires an accurate extrinsic calibration. The calibration of such complex systems is challenging, due to the number of sensors and modalities, and also due to the small overlapping fields of view between the sensors, which are positioned to capture different viewpoints of the cell. This paper proposes a sensor to pattern methodology that can calibrate a complex system such as a collaborative cell in a single optimization procedure. Our methodology can tackle RGB and Depth cameras, as well as LiDARs. Results show that our methodology is able to accurately calibrate a collaborative cell containing three RGB cameras, a depth camera and three 3D LiDARs.
	Address
	Corporate Author				Thesis
	Publisher	Science Direct	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MSIAU; MACO			Approved	no
	Call Number	Admin @ si @ ROS2022			Serial	3750
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