Feasibility Study of Textureless Object Detection and Pose Estimation Based on a Model with 3D Edgels and Surfaces

Abstract This paper describes a method for the detection of textureless objects. Our target objects include furniture and home appliances, which have no rich textural features or characteristic shapes. Focusing on the ease of application, we define a model that represents objects in terms of three-dimensional edgels and surfaces. Object detection is performed by superimposing input data on the model. A two-stage algorithm is applied to bring out object poses. Surfaces are used to extract candidates fromthe input data, and edgels are then used to identify the pose of a target object using two-dimensional template matching. Experiments using four real furniture and home appliances were performed to show the feasibility of the proposed method.We suggest the possible applicability in occlusion and clutter conditions.

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Recognition and pose estimation of unoccluded three-dimensional objects from a two-dimensional perspective view by banks of neural networks

IEEE Transactions on Neural Networks ◽

10.1109/72.508933 ◽

1996 ◽

Vol 7 (4) ◽

pp. 897-906 ◽

Cited By ~ 31

Author(s):

A. Khotanzad ◽

J.J.-H. Liou

Keyword(s):

Neural Networks ◽

Pose Estimation ◽

Three Dimensional ◽

Two Dimensional ◽

Perspective View ◽

Three Dimensional Objects

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Contextualised learning‐free three‐dimensional body pose estimation from two‐dimensional body features in monocular images

IET Computer Vision ◽

10.1049/iet-cvi.2015.0283 ◽

2016 ◽

Vol 10 (4) ◽

pp. 299-307

Author(s):

Luis Unzueta ◽

Nerea Aranjuelo ◽

Jon Goenetxea ◽

Mikel Rodriguez ◽

Maria Teresa Linaza

Keyword(s):

Pose Estimation ◽

Three Dimensional ◽

Two Dimensional ◽

Body Pose Estimation

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Two Dimensional Human Pose Estimation for the Upper Body Using Histogram and Template Matching Techniques

2013 1st International Conference on Artificial Intelligence, Modelling and Simulation ◽

10.1109/aims.2013.46 ◽

2013 ◽

Author(s):

Rosalyn R. Porle ◽

Ali Chekima ◽

Farrah Wong ◽

Mazlina Mamat ◽

Norfarariyanti Parimon ◽

...

Keyword(s):

Pose Estimation ◽

Template Matching ◽

Upper Body ◽

Human Pose Estimation ◽

Two Dimensional ◽

Human Pose ◽

Matching Techniques

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An Improved Algorithm for Detection and Pose Estimation of Texture-Less Objects

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2021.p0204 ◽

2021 ◽

Vol 25 (2) ◽

pp. 204-212

Author(s):

Jian Peng ◽

Ya Su ◽

◽

Keyword(s):

Pose Estimation ◽

Template Matching ◽

Target Object ◽

Test Image ◽

Image Region ◽

Multi Scale ◽

Training Stage ◽

Sensitivity Problem ◽

Depth Sensitivity ◽

Improved Algorithm

This paper introduces an improved algorithm for texture-less object detection and pose estimation in industrial scenes. In the template training stage, a multi-scale template training method is proposed to improve the sensitivity of LineMOD to template depth. When this method performs template matching, the test image is first divided into several regions, and then training templates with similar depth are selected according to the depth of each test image region. In this way, without traversing all the templates, the depth of the template used by the algorithm during template matching is kept close to the depth of the target object, which improves the speed of the algorithm while ensuring that the accuracy of recognition will not decrease. In addition, this paper also proposes a method called coarse positioning of objects. The method avoids a lot of useless matching operations, and further improves the speed of the algorithm. The experimental results show that the improved LineMOD algorithm in this paper can effectively solve the algorithm’s template depth sensitivity problem.

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Two-dimensional video-based analysis of human gait using pose estimation

10.1101/2020.07.24.218776 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jan Stenum ◽

Cristina Rossi ◽

Ryan T. Roemmich

Keyword(s):

Gait Analysis ◽

Motion Capture ◽

Pose Estimation ◽

Gold Standard ◽

Three Dimensional ◽

Human Gait ◽

Large Set ◽

Two Dimensional ◽

Gait Parameters ◽

Human Gait Analysis

ABSTRACTWalking is the primary mode of human locomotion. Accordingly, people have been interested in studying human gait since at least the fourth century BC. Human gait analysis is now common in many fields of clinical and basic research, but gold standard approaches – e.g., three-dimensional motion capture, instrumented mats or footwear, and wearables – are often expensive, immobile, data-limited, and/or require specialized equipment or expertise for operation. Recent advances in video-based pose estimation have suggested exciting potential for analyzing human gait using only two-dimensional video inputs collected from readily accessible devices (e.g., smartphones, tablets). However, we currently lack: 1) data about the accuracy of video-based pose estimation approaches for human gait analysis relative to gold standard measurement techniques and 2) an available workflow for performing human gait analysis via video-based pose estimation. In this study, we compared a large set of spatiotemporal and sagittal kinematic gait parameters as measured by OpenPose (a freely available algorithm for video-based human pose estimation) and three-dimensional motion capture from trials where healthy adults walked overground. We found that OpenPose performed well in estimating many gait parameters (e.g., step time, step length, sagittal hip and knee angles) while some (e.g., double support time, sagittal ankle angles) were less accurate. We observed that mean values for individual participants – as are often of primary interest in clinical settings – were more accurate than individual step-by-step measurements. We also provide a workflow for users to perform their own gait analyses and offer suggestions and considerations for future approaches.

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Two-dimensional video-based analysis of human gait using pose estimation

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008935 ◽

2021 ◽

Vol 17 (4) ◽

pp. e1008935

Author(s):

Jan Stenum ◽

Cristina Rossi ◽

Ryan T. Roemmich

Keyword(s):

Gait Analysis ◽

Motion Capture ◽

Pose Estimation ◽

Mean Absolute Error ◽

Three Dimensional ◽

Absolute Error ◽

Human Gait ◽

Two Dimensional ◽

Gait Parameters ◽

Human Gait Analysis

Human gait analysis is often conducted in clinical and basic research, but many common approaches (e.g., three-dimensional motion capture, wearables) are expensive, immobile, data-limited, and require expertise. Recent advances in video-based pose estimation suggest potential for gait analysis using two-dimensional video collected from readily accessible devices (e.g., smartphones). To date, several studies have extracted features of human gait using markerless pose estimation. However, we currently lack evaluation of video-based approaches using a dataset of human gait for a wide range of gait parameters on a stride-by-stride basis and a workflow for performing gait analysis from video. Here, we compared spatiotemporal and sagittal kinematic gait parameters measured with OpenPose (open-source video-based human pose estimation) against simultaneously recorded three-dimensional motion capture from overground walking of healthy adults. When assessing all individual steps in the walking bouts, we observed mean absolute errors between motion capture and OpenPose of 0.02 s for temporal gait parameters (i.e., step time, stance time, swing time and double support time) and 0.049 m for step lengths. Accuracy improved when spatiotemporal gait parameters were calculated as individual participant mean values: mean absolute error was 0.01 s for temporal gait parameters and 0.018 m for step lengths. The greatest difference in gait speed between motion capture and OpenPose was less than 0.10 m s−1. Mean absolute error of sagittal plane hip, knee and ankle angles between motion capture and OpenPose were 4.0°, 5.6° and 7.4°. Our analysis workflow is freely available, involves minimal user input, and does not require prior gait analysis expertise. Finally, we offer suggestions and considerations for future applications of pose estimation for human gait analysis.

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Mouth Movement Recognition Using Template Matching and its Implementation in an Intelligent Room

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2012.p0311 ◽

2012 ◽

Vol 24 (2) ◽

pp. 311-319

Author(s):

Kiyoshi Takita ◽

◽

Takeshi Nagayasu ◽

Hidetsugu Asano ◽

Kenji Terabayashi ◽

...

Keyword(s):

Dynamic Programming ◽

Similarity Measure ◽

Template Matching ◽

Target Object ◽

Facial Region ◽

Mouth Region ◽

Home Appliances ◽

Movement Recognition ◽

Template Image ◽

Mouth Movement

This paper proposes a method of recognizing movements of the mouth from images and implements the method in an intelligent room. The proposed method uses template matching and recognizes mouth movements for the purpose of indicating a target object in an intelligent room. First, the operator’s face is detected. Then, the mouth region is extracted from the facial region using the result of template matching with a template image of the lips. Dynamic Programming (DP) matching is applied to a similarity measure that is obtained by template matching. The effectiveness of the proposed method is evaluated through experiments to recognize several names of common home appliances and operations.

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