Single Camera Models and Calibration Procedures in Computer Vision

2009 ◽  
pp. 1-37
Author(s):  
Michael A. Michael A. ◽  
Jean-José Orteu ◽  
Hubert W. Schreier
Keyword(s):  
Computer Vision ◽  
Single Camera

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.

scholarly journals Comparative Study of Relative-Pose Estimations from a Monocular Image Sequence in Computer Vision and Photogrammetry

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1905 ◽  
Author(s):  
Tserennadmid Tumurbaatar ◽  
Taejung Kim
Keyword(s):  
Computer Vision ◽  
Image Sequences ◽  
Consumer Electronics ◽  
Estimation Methods ◽  
Single Camera ◽  
Essential Matrix ◽  
3D Motion ◽  
Monocular Image ◽  
Motion Parameters ◽  
Relative Pose

Techniques for measuring the position and orientation of an object from corresponding images are based on the principles of epipolar geometry in the computer vision and photogrammetric fields. Contributing to their importance, many different approaches have been developed in computer vision, increasing the automation of the pure photogrammetric processes. The aim of this paper is to evaluate the main differences between photogrammetric and computer vision approaches for the pose estimation of an object from image sequences, and how these have to be considered in the choice of processing technique when using a single camera. The use of a single camera in consumer electronics has enormously increased, even though most 3D user interfaces require additional devices to sense 3D motion for their input. In this regard, using a monocular camera to determine 3D motion is unique. However, we argue that relative pose estimations from monocular image sequences have not been studied thoroughly by comparing both photogrammetry and computer vision methods. To estimate motion parameters characterized by 3D rotation and 3D translations, estimation methods developed in the computer vision and photogrammetric fields are implemented. This paper describes a mathematical motion model for the proposed approaches, by differentiating their geometric properties and estimations of the motion parameters. A precision analysis is conducted to investigate the main characteristics of the methods in both fields. The results of the comparison indicate the differences between the estimations in both fields, in terms of accuracy and the test dataset. We show that homography-based approaches are more accurate than essential-matrix or relative orientation–based approaches under noisy conditions.

scholarly journals Analysis of futsal matches using a single-camera computer vision system

2020 ◽  
Author(s):  
Heloiza Paulichen ◽  
Kallil Zielinski ◽  
Dalcimar Casanova ◽  
Pablo Cavalcanti
Keyword(s):  
Computer Vision ◽  
Data Visualization ◽  
Real World ◽  
Vision System ◽  
Single Camera ◽  
Wide Angle ◽  
Computer Vision System ◽  
Video Recordings ◽  
Wide Angle Lens ◽  
Or Team

The use of computer systems in sports has increased significantly in the last decade. Consequently, systems have been developed to help each athlete or team quantify their performance, such as distances traveled, speeds attained, and positions where each athlete was on the court or field. In this work, a method based on computer vision is proposed to analyse futsal matches. Videos were acquired using a single camera with a wide-angle lens, which facilitates the installation and calibration process in different matches and arenas. The approach is illustrated through video recordings of Pato Futsal team, from which the athletes were detected, their positions projected from pixels to real world coordinates their trajectories estimated. The generated data visualization aims to help coaches in their physical and tactical analysis.

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