Daniel Ponsa, & Antonio Lopez. (2007). Cascade of Classifiers for Vehicle Detection. In Advanced Concepts for Intelligent Vision Systems, LNCS 4678, volume 1, pp. 980–989.
Keywords: vehicle detection
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Daniel Ponsa, & Antonio Lopez. (2009). Variance reduction techniques in particle-based visual contour Tracking. PR - Pattern Recognition, 42(11), 2372–2391.
Abstract: This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done.
Keywords: Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling
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Daniel Ponsa, & Antonio Lopez. (2009). Seguimiento Visual de Contornos Computerizado.
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Daniel Ponsa, Antonio Lopez, Felipe Lumbreras, Joan Serrat, & T. Graf. (2005). 3D Vehicle Sensor based on Monocular Vision.
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Daniel Ponsa, Antonio Lopez, Joan Serrat, Felipe Lumbreras, & T. Graf. (2005). Multiple Vehicle 3D Tracking Using an Unscented Kalman Filter.
Keywords: vehicle detection
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Daniel Ponsa, Joan Serrat, & Antonio Lopez. (2011). On-board image-based vehicle detection and tracking. TIM - Transactions of the Institute of Measurement and Control, 33(7), 783–805.
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.
Keywords: vehicle detection
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Daniel Ponsa, & Jordi Vitria. (1999). Mobile monitoring system using an agent-oriented approach.
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Daniel Ponsa, Robert Benavente, Felipe Lumbreras, Judit Martinez, & Xavier Roca. (2003). Quality control of safety belts by machine vision inspection for real-time production. Optical Engineering (IF: 0.877), 42(4), 1114–1120.
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Daniel Ponsa, & Xavier Roca. (2002). A Novel Approach to Generate Multiple Shape Models..
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Daniel Ponsa, & Xavier Roca. (2002). Unsupervised Parameterisation of Gaussian Mixture Models.
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Daniel Ponsa, & Xavier Roca. (2003). Multiple Model Approach to Deformable Shape Tracking.
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Daniel Sanchez, J.C.Ortega, & Miguel Angel Bautista. (2013). Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization. In 6th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 7887, pp. 50–58). LNCS. Springer Berlin Heidelberg.
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.
Keywords: Human Body Segmentation; Error-Correcting Output Codes; Cascade of Classifiers; Graph Cuts
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Daniel Sanchez, Meysam Madadi, Marc Oliu, & Sergio Escalera. (2019). Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation. In 14th IEEE International Conference on Automatic Face and Gesture Recognition.
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.
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Daniel Sanchez, Miguel Angel Bautista, & Sergio Escalera. (2015). HuPBA 8k+: Dataset and ECOC-GraphCut based Segmentation of Human Limbs. NEUCOM - Neurocomputing, 150(A), 173–188.
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.
Keywords: Human limb segmentation; ECOC; Graph-Cuts
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Daniela Rato, Miguel Oliveira, Vitor Santos, Manuel Gomes, & Angel Sappa. (2022). A sensor-to-pattern calibration framework for multi-modal industrial collaborative cells. JMANUFSYST - Journal of Manufacturing Systems, 64, 497–507.
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.
Keywords: Calibration; Collaborative cell; Multi-modal; Multi-sensor
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