Hierarchical fiducial marker design for pose estimation in large-scale scenarios

2018 ◽  
Vol 35 (6) ◽  
pp. 835-849 ◽  
Author(s):  
Hao Wang ◽  
Zongying Shi ◽  
Geng Lu ◽  
Yisheng Zhong
Keyword(s):  
Pose Estimation ◽  
Large Scale ◽  
Fiducial Marker ◽  
Marker Design

scholarly journals Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods

2021 ◽  
Vol 10 (11) ◽  
pp. 748
Author(s):  
Ferdinand Maiwald ◽  
Christoph Lehmann ◽  
Taras Lazariv
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Image Retrieval ◽  
Pose Estimation ◽  
Large Scale ◽  
Feature Matching ◽  
Geographic Information ◽  
Content Based Image Retrieval ◽  
Matching Methods ◽  
Historical Images

The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models.

scholarly journals Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6940
Author(s):  
Elise Klæbo Vonstad ◽  
Xiaomeng Su ◽  
Beatrix Vereijken ◽  
Kerstin Bach ◽  
Jan Harald Nilsen
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Gold Standard ◽  
Large Scale ◽  
Statistical Significance ◽  
Balance Training ◽  
Home Exercise ◽  
Rank Test ◽  
Camera System ◽  
Estimation System

Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (i.e., variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N = 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon’s signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise.

SCALE ROBUST HEAD POSE ESTIMATION BASED ON RELATIVE HOMOGRAPHY TRANSFORMATION

2014 ◽  
Vol 10 (01) ◽  
pp. 69-90 ◽  
Author(s):  
CHENGUANG LIU ◽  
HENGDA CHENG ◽  
ARAVIND DASU
Keyword(s):  
Pose Estimation ◽  
Large Scale ◽  
Region Of Interest ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Virtual Camera ◽  
The Face ◽  
Large Scale Change ◽  
Chamfer Matching ◽  
Homography Transformation

Head pose estimation has been widely studied in recent decades due to many significant applications. Different from most of the current methods which utilize face models to estimate head position, we develop a relative homography transformation based algorithm which is robust to the large scale change of the head. In the proposed method, salient Harris corners are detected on a face, and local binary pattern features are extracted around each of the corners. And then, relative homography transformation is calculated by using RANSAC optimization algorithm, which applies homography to a region of interest (ROI) on an image and calculates the transformation of a planar object moving in the scene relative to a virtual camera. By doing so, the face center initialized in the first frame will be tracked frame by frame. Meanwhile, a head shoulder model based Chamfer matching method is proposed to estimate the head centroid. With the face center and the detected head centroid, the head pose is estimated. The experiments show the effectiveness and robustness of the proposed algorithm.

Multi-Scale Collaborative Network for Human Pose Estimation

2019 ◽  
Vol 16 (04) ◽  
pp. 1941003
Author(s):  
Chunsheng Guo ◽  
Jialuo Zhou ◽  
Wenlong Du ◽  
Xuguang Zhang
Keyword(s):  
Loss Function ◽  
Pose Estimation ◽  
Large Scale ◽  
Human Pose Estimation ◽  
Small Scale ◽  
Feature Maps ◽  
Multi Scale ◽  
Human Pose ◽  
Processing Network ◽  
Weight Coefficients

Human pose estimation is a fundamental but challenging task in computer vision. The estimation of human pose mainly depends on the global information of the keypoint type and the local information of the keypoint location. However, the consistency of the cascading process makes it difficult for each stacking network to form a differentiation and collaboration mechanism. In order to solve these problems, this paper introduces a new human pose estimation framework called Multi-Scale Collaborative (MSC) network. The pre-processing network forms feature maps of different sizes, and dispatches them to various locations of the stack network, with small-scale features reaching the front-end stacking network and large-scale features reaching the back-end stacking network. A new loss function is proposed for MSC network. Different keypoints have different weight coefficients of loss function at different scales, and the keypoint weight coefficients are dynamically adjusted from the top hourglass network to the bottom hourglass network. Experimental results show that the proposed method is competitive in MPII and LSP challenge leaderboard among the state-of-the-art methods.

scholarly journals Pose Estimation of a Noncooperative Target Based on Monocular Visual SLAM

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ting Lei ◽  
Xiao-Feng Liu ◽  
Guo-Ping Cai ◽  
Yun-Meng Liu ◽  
Pan Liu
Keyword(s):  
Pose Estimation ◽  
Large Scale ◽  
Direct Method ◽  
Estimation Methods ◽  
Current Frame ◽  
Feature Based ◽  
Simultaneous Location And Mapping ◽  
Slam Algorithm ◽  
Local Map ◽  
A Direct Method

This paper estimates the pose of a noncooperative space target utilizing a direct method of monocular visual simultaneous location and mapping (SLAM). A Large Scale Direct SLAM (LSD-SLAM) algorithm for pose estimation based on photometric residual of pixel intensities is provided to overcome the limitation of existing feature-based on-orbit pose estimation methods. Firstly, new sequence images of the on-orbit target are continuously inputted, and the pose of each current frame is calculated according to minimizing the photometric residual of pixel intensities. Secondly, frames are distinguished as keyframes or normal frames according to the pose relationship, and these frames are used to optimize the local map points. After that, the optimized local map points are added to the back-end map. Finally, the poses of keyframes are further enumerated and optimized in the back-end thread based on the map points and the photometric residual between the keyframes. Numerical simulations and experiments are carried out to prove the validity of the proposed algorithm, and the results elucidate the effectiveness of the algorithm in estimating the pose of the noncooperative target.

scholarly journals Rapture facilitates inexpensive and high-throughput parent-based tagging in salmonids

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0239221
Author(s):  
Michelle Y. Pepping ◽  
Sean M. O’Rourke ◽  
Connie Huang ◽  
Jacob V. E. Katz ◽  
Carson Jeffres ◽  
...  
Keyword(s):  
Large Scale ◽  
Oncorhynchus Tshawytscha ◽  
Restriction Site ◽  
Genetic Relationships ◽  
Cost Effective ◽  
Nucleotide Polymorphisms ◽  
Parentage Assignment ◽  
Aquaculture Management ◽  
Marker Design ◽  
Specific Restriction

Accurate methods for tracking individuals are crucial to the success of fisheries and aquaculture management. Management of migratory salmonid populations, which are important for the health of many economies, ecosystems, and indigenous cultures, is particularly dependent on data gathered from tagged fish. However, the physical tagging methods currently used have many challenges including cost, variable marker retention, and information limited to tagged individuals. Genetic tracking methods combat many of the problems associated with physical tags, but have their own challenges including high cost, potentially difficult marker design, and incompatibility of markers across species. Here we show the feasibility of a new genotyping method for parent-based tagging (PBT), where individuals are tracked through the inherent genetic relationships with their parents. We found that Rapture sequencing, a combination of restriction-site associated DNA and capture sequencing, provides sufficient data for parentage assignment. Additionally, the same capture bait set, which targets specific restriction-site associated DNA loci, can be used for both Rainbow Trout Oncorhynchus mykiss and Chinook Salmon Oncorhynchus tshawytscha. We input 248 single nucleotide polymorphisms from 1,121 samples to parentage assignment software and compared parent-offspring relationships of the spawning pairs recorded in a hatchery. Interestingly, our results suggest sperm contamination during hatchery spawning occurred in the production of 14% of offspring, further confirming the need for genetic tagging in accurately tracking individuals. PBT with Rapture successfully assigned progeny to parents with a 98.86% accuracy with sufficient genetic data. Cost for this pilot study was approximately $3 USD per sample. As costs vary based on the number of markers used and individuals sequenced, we expect that when implemented at a large-scale, per sample costs could be further decreased. We conclude that Rapture PBT provides a cost-effective and accurate alternative to the physical coded wire tags, and other genetic-based methods.

Recognition and 6D Pose Estimation of Large-scale Objects using 3D Semi-Global Descriptors

2019 ◽  
Author(s):  
David Nospes ◽  
Kirill Safronov ◽  
Sarah Gillet ◽  
Klaus Brillowski ◽  
Uwe E. Zimmermann
Keyword(s):  
Pose Estimation ◽  
Large Scale
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