A computer-vision method to estimate joint angles and L5/S1 moments during lifting tasks through a single camera

2021 ◽  
pp. 110860
Author(s):  
Hanwen Wang ◽  
Ziyang Xie ◽  
Lu Lu ◽  
Li Li ◽  
Xu Xu
Keyword(s):  
Computer Vision ◽  
Single Camera ◽  
Joint Angles

The measurement of in vivo joint angles during a squat using a single camera markerless motion capture system as compared to a marker based system

2015 ◽  
Vol 41 (2) ◽  
pp. 694-698 ◽  
Author(s):  
Anne Schmitz ◽  
Mao Ye ◽  
Grant Boggess ◽  
Robert Shapiro ◽  
Ruigang Yang ◽  
...  
Keyword(s):  
Motion Capture ◽  
Single Camera ◽  
Motion Capture System ◽  
Joint Angles ◽  
Markerless Motion Capture

Multi-Object Tracking with Single Camera

2015 ◽  
Vol 740 ◽  
pp. 668-671
Author(s):  
Yu Bing Dong ◽  
Ying Sun ◽  
Ming Jing Li
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Block Matching ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Single Camera ◽  
Trajectory Prediction ◽  
Tracking Method

Multi-object tracking has been a challenging topic in computer vision. A Simple and efficient moving multi-object tracking algorithm is proposed. A new tracking method combined with trajectory prediction and a sub-block matching is used to handle the objects occlusion. The experimental results show that the proposed algorithm has good performance.

scholarly journals Computer Vision Based 3D Reconstruction : A Review

2019 ◽  
Vol 9 (4) ◽  
pp. 2394 ◽  
Author(s):  
Hanry Ham ◽  
Julian Wesley ◽  
Hendra Hendra
Keyword(s):  
Computer Vision ◽  
3D Reconstruction ◽  
Structured Light ◽  
Time Of Flight ◽  
Single Camera ◽  
Object Reconstruction ◽  
Sea Levels ◽  
Stereo Camera ◽  
3D Data ◽  
Fusion Approach

3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.

scholarly journals Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser

2021 ◽  
Vol 11 (2) ◽  
pp. 488
Author(s):  
Linshen Yao ◽  
Haibo Liu
Keyword(s):  
Computer Vision ◽  
Surface Reconstruction ◽  
High Accuracy ◽  
Measurement Technology ◽  
Single Camera ◽  
One Dimensional ◽  
Contact Measurement ◽  
Laser Plane ◽  
On Line ◽  
Telecentric Lens

Non-contact measurement technology based on triangulation with cameras is extensively applied to the development of computer vision. However, the accuracy of the technology is generally not satisfactory enough. The application of telecentric lenses can significantly improve the accuracy, but the view of telecentric lenses is limited due to their structure. To address these challenges, a telecentric surface reconstruction system is designed for surface detection, which consists of a single camera with a telecentric lens, line laser generator and one-dimensional displacement platform. The designed system can reconstruct the surface with high accuracy. The measured region is expanded with the used of the displacement platform. To achieve high-accuracy surface reconstruction, we propose a method based on a checkerboard to calibrate the designed system, including line laser plane and motor direction of the displacement platform. Based on the calibrated system, the object under the line laser is measured, and the results of lines are assembled to make the final surface reconstruction. The results show that the designed system can reconstruct a region of 20×40 mm2, up to the accuracy of micron order.

Stereo Vision-Based Object Matching, Detection, and Tracking

2013 ◽  
pp. 91-117
Author(s):  
Mohamed Saifuddin ◽  
Lee Seng Yeong ◽  
Seng Kah Phooi ◽  
Ang Li-Minn
Keyword(s):  
Computer Vision ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Unmanned Vehicles ◽  
Human Vision ◽  
Surveillance Systems ◽  
Single Camera ◽  
Important Research ◽  
3 Dimensional ◽  
Detection And Tracking

Computer vision has become very important in recent years. It is no longer restricted to a single camera that is only capable of capturing a single image at any given time. In its place, stereo vision systems have been introduced that not only make use of dual cameras to capture multiple images at once, but they also simulate the exact same nature of the human eye vision. Stereo vision has turned out to be an important research component in the subdivision of computer vision and image processing that deals with the extraction of information from images for the purpose of video surveillance systems, mimicking the human vision for the visually impaired, for robotics, to control unmanned vehicles, for security purposes, virtual reality and 3 Dimensional (3D) televisions, etc. In this chapter, a comprehensive review of all recent algorithms such as stereo matching, object detection, tracking techniques for stereo vision are presented.

scholarly journals Wide-Angle, Monocular Head Tracking using Passive Markers

2021 ◽  
Author(s):  
Balazs P Vagvolgyi ◽  
Ravikrishnan P Jayakumar ◽  
Manu S Madhav ◽  
James J Knierim ◽  
Noah Cowan
Keyword(s):  
Computer Vision ◽  
3D Reconstruction ◽  
Visual Information ◽  
Small Animal ◽  
Wide Field ◽  
Head Tracking ◽  
Single Camera ◽  
Wide Range ◽  
Position And Orientation ◽  
Set Up

Camera images can encode large amounts of visual information of an animal and its environment, enabling high fidelity 3D reconstruction of the animal and its environment using computer vision methods. Most systems, both markerless (e.g. deep learning based) and marker-based, require multiple cameras to track features across multiple points of view to enable such 3D reconstruction. However, such systems can be expensive and are challenging to set up in small animal research apparatuses. We present an open-source, marker-based system for tracking the head of a rodent for behavioral research that requires only a single camera with a potentially wide field of view. The system features a lightweight visual target and computer vision algorithms that together enable high-accuracy tracking of the six-degree-of-freedom position and orientation of the animal's head. The system, which only requires a single camera positioned above the behavioral arena, robustly reconstructs the pose over a wide range of head angles (360 degrees in yaw, and approximately +/-120 degrees in roll and pitch). Experiments with live animals demonstrate that the system can reliably identifyrat head position and orientation. Evaluations using a commercial optical tracker device show that the system achieves accuracy that rivals commercial multi-camera systems. Our solution significantly improves upon existing monocular marker-based tracking methods, both in accuracy and in allowable range of motion. The proposed system enables the study of complex behaviors by providing robust, fine-scale measurements of rodent head motions in a wide range of orientations.

Stereo Vision-Based Object Matching, Detection, and Tracking

2017 ◽  
pp. 440-469
Author(s):  
Mohamed Saifuddin ◽  
Lee Seng Yeong ◽  
Seng Kah Phooi ◽  
Ang Li-Minn
Keyword(s):  
Computer Vision ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Unmanned Vehicles ◽  
Human Vision ◽  
Surveillance Systems ◽  
Single Camera ◽  
Important Research ◽  
3 Dimensional ◽  
Detection And Tracking

Computer vision has become very important in recent years. It is no longer restricted to a single camera that is only capable of capturing a single image at any given time. In its place, stereo vision systems have been introduced that not only make use of dual cameras to capture multiple images at once, but they also simulate the exact same nature of the human eye vision. Stereo vision has turned out to be an important research component in the subdivision of computer vision and image processing that deals with the extraction of information from images for the purpose of video surveillance systems, mimicking the human vision for the visually impaired, for robotics, to control unmanned vehicles, for security purposes, virtual reality and 3 Dimensional (3D) televisions, etc. In this chapter, a comprehensive review of all recent algorithms such as stereo matching, object detection, tracking techniques for stereo vision are presented.

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