•A new approach for generation of polygonal approximations based on the convex hull of contour is proposed.•The proposed algorithm takes into account the symmetry of the contour.•A final improvement ...process is applied to increase the quality of the polygonal approximation.•The new algorithm is non-optimal but unsupervised (automatic), because no parameters have to be set or tuned.•Experiments using a public available dataset show that the new proposal outperforms other unsupervised algorithms.
The present paper proposes a new non-optimal but unsupervised algorithm, called ICT-RDP, for generation of polygonal approximations based on the convex hull. Firstly, the new algorithm takes into account the convex hull of the 2D closed curves or contours to select a set of initial points; secondly, the significance levels of the contour points are computed using a symmetric version of the well-known Ramer, Douglas-Peucker algorithm; and, finally, a thresholding process is applied to obtain the vertices or dominant points of the polygonal approximation. Since the convex hull can select many initial points in rounded parts of the contour, an additional deletion process is required to remove quasi-collinear dominant points. Furthermore, an additional improvement process is applied to shift the dominant points in order to increase the quality of the polygonal approximation. Experiments performed on a public available dataset show that the new proposal outperforms other unsupervised algorithms for generation of polygonal approximations.
Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors’ ability to monitor ...patients’ recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress. Doctors use the flexion angle of limbs as a cue to assess a patient’s mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject’s position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints.
Fiducial Objects: Custom Design and Evaluation García-Ruiz, Pablo; Romero-Ramirez, Francisco J; Muñoz-Salinas, Rafael ...
Sensors (Basel, Switzerland),
12/2023, Letnik:
23, Številka:
24
Journal Article
Recenzirano
Odprti dostop
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.
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.
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.
•A system that speed up the detection of squared planar markers•Employ a multiresolution approach to reduce computing time•Temporal information is also considered to reduce computation.•Up to 40 times faster than the state-of-the-art approaches without any parallelization
Fiducial markers such as QR codes, ArUco, and AprilTag have become very popular tools for labeling and camera positioning. They are robust and easy to detect, even in devices with low computing ...power. However, their industrial appearance deters their use in scenarios where an attractive and visually appealing look is required. In these cases, it would be preferable to use customized markers showing, for instance, a company logo. This work proposes a novel method to design, detect, and track customizable fiducial markers. Our work allows creating markers templates imposing few restrictions on its design, e.g., a company logo or a picture can be used. The designer must indicate positions into the template where bits will encode a unique identifier for each marker. Then, our method will automatically create a dictionary of markers, all following the same design, but each with a unique identifier. Finally, we propose a method for detecting and tracking the markers even under occlusion, which is not allowed in traditional fiducial markers. The experiments conducted show that the performance of the customizable markers is similar to the best traditional markers systems without significantly sacrificing speed.
This study targets 2D articulated human pose estimation (i.e. localisation of body limbs) in stereo videos. Although in recent years depth-based devices (e.g. Microsoft Kinect) have gained ...popularity, as they perform very well in controlled indoor environments (e.g. living rooms, operating theatres or gyms), they suffer clear problems in outdoor scenarios and, therefore, human pose estimation is still an interesting unsolved problem. The authors propose here a novel approach that is able to localise upper-body keypoints (i.e. shoulders, elbows, and wrists) in temporal sequences of stereo image pairs. The authors’ method starts by locating and segmenting people in the image pairs by using disparity and appearance information. Then, a set of candidate body poses is computed for each view independently. Finally, temporal and stereo consistency is applied to estimate a final 2D pose. The authors’ validate their model on three challenging datasets: ‘stereo human pose estimation dataset’, ‘poses in the wild’ and ‘INRIA 3DMovie’. The experimental results show that the authors’ model not only establishes new state-of-the-art results on stereo sequences, but also brings improvements in monocular sequences.
Event cameras are a new type of image sensors that output changes in light intensity (events) instead of absolute intensity values. They have a very high temporal resolution and a high dynamic range. ...In this paper, we propose a method to detect and decode binary square markers using an event camera. We detect the edges of the markers by detecting line segments in an image created from events in the current packet. The line segments are combined to form marker candidates. The bit value of marker cells is decoded using the events on their borders. To the best of our knowledge, no other approach exists for detecting square binary markers directly from an event camera. Experimental results show that the performance of our proposal is much superior to the one from the RGB ArUco marker detector. Additionally, the proposed method can run on a single CPU thread in real-time.
Detecting and tracking persons in the sequences of monocular images are the important and difficult problems in computer vision and have been well studied in these two decades. Recently, the methods ...based on stereo vision have attracted great attentions since 3D information can be exploited. This paper presents an approach for multiple-people detection and tracking using stereo vision. Tracking is carried out using a multiple particle filtering approach that combines depth, colour and gradient information. We modify the degree of confidence assigned to depth information, according to the amount of it found in the disparity map, using a novel confidence measure. The greater the amount of disparity information found, the higher the degree of confidence assigned to depth information in the final particles weights is. In the worst case (total absence of disparity), the proposed algorithm makes use of the information available (colour and gradient) to track, thus performing as a pure colour-based tracking algorithm. People are detected combining an adaboost classifier with stereo information. In order to test the validity of our proposal, it is evaluated in several sequences of colour and disparity images where people interact in complex situations: walk at different distances, shake hands, cross their paths, jump, run, embrace each other and even swap their positions quickly trying to confuse the system. The experimental results show that the proposal is able to deal with occlusions and to effectively determine both the 3D position of the people being tracked and their 2D head locations in the camera image, and everything is realized in real time. Besides, as the proposed method does not require the use of a background model, it can be considered particularly appropriate for applications that must run on mobile devices.
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.
•Novel approach for estimating the location of fiducial markers using Discriminative Correlation Filters (DCF).•Camera localization with marker maps employing a predictive approach.•Proposed method is fastest and more robust to blur than the state-of-the-art marker detection algorithms.•The method has been compared to the state-of-the-art SLAM methods obtaining, better accuracy, sensitivity, and speed.
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