Examinando por Autor "Romero-Ramirez, Francisco J."

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3D human pose estimation from depth maps using a deep combination of poses
(Elsevier, 2018-08) Marín-Jiménez, Manuel J.; Romero-Ramirez, Francisco J.; Muñoz-Salinas, Rafael; Medina-Carnicer, Rafael
Many real-world applications require the estimation of human body joints for higher-level tasks as, for example, human behaviour understanding. In recent years, depth sensors have become a popular approach to obtain three-dimensional information. The depth maps generated by these sensors provide information that can be employed to disambiguate the poses observed in two-dimensional images. This work addresses the problem of 3D human pose estimation from depth maps employing a Deep Learning approach. We propose a model, named Deep Depth Pose (DDP), which receives a depth map containing a person and a set of predefined 3D prototype poses and returns the 3D position of the body joints of the person. In particular, DDP is defined as a ConvNet that computes the specific weights needed to linearly combine the prototypes for the given input. We have thoroughly evaluated DDP on the challenging ‘ITOP’ and ‘UBC3V’ datasets, which respectively depict realistic and synthetic samples, defining a new state-of-the-art on them
Determination of forest fuels characteristics in mortality-affected Pinus forests using integrated hyperspectral and ALS data
(Elsevier, 2018-06) Romero-Ramirez, Francisco J.; Navarro-Cerrillo, Rafael Mª; Quero, Jose Luis; Doerr, Stefan; Rocío, Hernández-Clemente; Varo-Martínez, Mª Ángeles
Widespread tree mortality caused by forest decline in recent decades has raised concern among forest managers about how to assess forest fuels in these conditions. To investigate this question, we developed and tested an objective, consistent approach to the characterization of canopy fuel metrics – such as fuel load (FL), live fuel moisture content (LFMC), and live-dead ratio (LDR) – by integrating airborne laser scanning (ALS) and hyperspectral data to produce more-accurate estimates at the stand level. Regression models were developed for Pinus sylvestris and P. nigra stands representative of pine plantations in southern Spain, using field data acquired for different spatial fuel types and distributions as well as high resolution airborne hyperspectral data (AHS) and ALS datasets. Strong relationships were found between ALS and FL using a density of 2 points m−2 (R2 = 0.64) and between LFMC and Temperature/NDVI index at a spatial resolution of 5 m (R2 = 0.91). The red edge normalized index provided the highest separability (Jeffries-Matusita distance = 1.83) between types of LDR. The plot-aggregate ALS and AHS metrics performed better at spatial resolutions of 5 m and 2 points m−2 than at other scales. Cartography of the estimations of FL, LFMC, and LDR made using the empirical models from the ALS and AHS data showed a mean FL value of 65.87 Mg ha−1, an average LFMC content of 57.51%, and 30.75% of the surface classified as dead fuel (≥60% defoliation). The results suggest that our remote sensing approach could improve the estimation of canopy fuels characteristics at higher spatial resolutions as well as estimations of fuel cartography, to assist the planning and management of fuel reduction treatments
Fiducial Objects: Custom Design and Evaluation
(MDPI, 2023-12) García-Ruiz, Pablo; Romero-Ramirez, Francisco J.; Muñoz-Salinas, Rafael; Marín-Jiménez, Manuel J.; Medina-Carnicer, Rafael
Camera pose estimation is vital in fields like robotics, medical imaging, and augmented reality. Fiducial markers, specifically ArUco and Apriltag, are preferred for their efficiency. However, their accuracy and viewing angle are limited when used as single markers. Custom fiducial objects have been developed to address these limitations by attaching markers to 3D objects, enhancing visibility from multiple viewpoints and improving precision. Existing methods mainly use square markers on non-square object faces, leading to inefficient space use. This paper introduces a novel approach for creating fiducial objects with custom-shaped markers that optimize face coverage, enhancing space utilization and marker detectability at greater distances. Furthermore, we present a technique for the precise configuration estimation of these objects using multiviewpoint images. We provide the research community with our code, tutorials, and an application to facilitate the building and calibration of these objects. Our empirical analysis assesses the effectiveness of various fiducial objects for pose estimation across different conditions, such as noise levels, blur, and scale variations. The results suggest that our customized markers significantly outperform traditional square markers, marking a positive advancement in fiducial marker-based pose estimation methods.
Fractal markers: A new approach for long-range marker pose estimation under occlusion
(Institute of Electrical and Electronics Engineers, 2019-11) Romero-Ramirez, Francisco J.; Munoz-Salinas, Rafael; Medina-Carnicer, Rafael
Squared fiducial markers are a powerful tool for camera pose estimation in applications such as robots, unmanned vehicles and augmented reality. The four corners of a single marker are enough to estimate the pose of a calibrated camera. However, they have some limitations. First, the methods proposed for detection are ineffective under occlusion. A small occlusion in any part of the marker makes it undetectable. Second, the range at which they can be detected is limited by their size. Very big markers can be detected from a far distance, but as the camera approaches them, they are not fully visible, and thus they can not be detected. Small markers, however, can not be detected from large distances. This paper proposes solutions to the above-mentioned problems. We propose the Fractal Marker, a novel type of marker that is built as an aggregation of squared markers, one into another, in a recursive manner. Also, we proposed a novel method for detecting Fractal Markers under severe occlusions. The results of our experiments show that the proposed method achieves a wider detection range than traditional markers and great robustness to occlusion
Speeded up detection of squared fiducial markers
(Elsevier, 2018-08) Romero-Ramirez, Francisco J.; Muñoz-Salinas, Rafael; Medina-Carnicer, Rafael
Squared planar markers have become a popular method for pose estimation in applications such as autonomous robots, unmanned vehicles and virtual trainers. The markers allow estimating the position of a monocular camera with minimal cost, high robustness, and speed. One only needs to create markers with a regular printer, place them in the desired environment so as to cover the working area, and then registering their location from a set of images. Nevertheless, marker detection is a time-consuming process, especially as the image dimensions grows. Modern cameras are able to acquire high resolutions images, but fiducial marker systems are not adapted in terms of computing speed. This paper proposes a multi-scale strategy for speeding up marker detection in video sequences by wisely selecting the most appropriate scale for detection, identification and corner estimation. The experiments conducted show that the proposed approach outperforms the state-of-the-art methods without sacrificing accuracy or robustness. Our method is up to 40 times faster than the state-of-the-art method, achieving over 1000 fps in 4 K images without any parallelization
sSLAM: Speeded-Up Visual SLAM Mixing Artificial Markers and Temporary Keypoints
(MDPI, 2023-02-16) Romero-Ramirez, Francisco J.; Muñoz-Salinas, Rafael; Marín-Jiménez, Manuel J.; Cazorla, Miguel; Medina-Carnicer, Rafael
Environment landmarks are generally employed by visual SLAM (vSLAM) methods in the form of keypoints. However, these landmarks are unstable over time because they belong to areas that tend to change, e.g., shadows or moving objects. To solve this, some other authors have proposed the combination of keypoints and artificial markers distributed in the environment so as to facilitate the tracking process in the long run. Artificial markers are special elements (similar to beacons) that can be permanently placed in the environment to facilitate tracking. In any case, these systems keep a set of keypoints that is not likely to be reused, thus unnecessarily increasing the computing time required for tracking. This paper proposes a novel visual SLAM approach that efficiently combines keypoints and artificial markers, allowing for a substantial reduction in the computing time and memory required without noticeably degrading the tracking accuracy. In the first stage, our system creates a map of the environment using both keypoints and artificial markers, but once the map is created, the keypoints are removed and only the markers are kept. Thus, our map stores only long-lasting features of the environment (i.e., the markers). Then, for localization purposes, our algorithm uses the marker information along with temporary keypoints created just in the time of tracking, which are removed after a while. Since our algorithm keeps only a small subset of recent keypoints, it is faster than the state-of-the-art vSLAM approaches. The experimental results show that our proposed sSLAM compares favorably with ORB-SLAM2, ORB-SLAM3, OpenVSLAM and UcoSLAM in terms of speed, without statistically significant differences in accuracy
Tracking fiducial markers with discriminative correlation filters
(Elsevier, 2021-03) Romero-Ramirez, Francisco J.; Muñoz-Salinas, Rafael; Medina-Carnicer, Rafael
In the last few years, squared fiducial markers have become a popular and efficient tool to solve monocular localization and tracking problems at a very low cost. Nevertheless, marker detection is affected by noise and blur: small camera movements may cause image blurriness that prevents marker detection. The contribution of this paper is two-fold. First, it proposes a novel approach for estimating the location of markers in images using a set of Discriminative Correlation Filters (DCF). The proposed method outperforms state-of-the-art methods for marker detection and standard DCFs in terms of speed, precision, and sensitivity. Our method is robust to blur and scales very well with image resolution, obtaining more than 200fps in HD images using a single CPU thread. As a second contribution, this paper proposes a method for camera localization with marker maps employing a predictive approach to detect visible markers with high precision, speed, and robustness to blurriness. The method has been compared to the state-of-the-art SLAM methods obtaining, better accuracy, sensitivity, and speed. The proposed approach is publicly available as part of the ArUco library