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Relevant bibliographies by topics / Kanade-Lucas-Tomasi algorithm

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Academic literature on the topic 'Kanade-Lucas-Tomasi algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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Journal articles on the topic "Kanade-Lucas-Tomasi algorithm"

1

Aashish, Kamath, and A. Vijayalakshmi. "Comparison of Viola-Jones And Kanade-Lucas-Tomasi Face Detection Algorithms." Oriental journal of computer science and technology 10, no. 1 (2017): 151–59. http://dx.doi.org/10.13005/ojcst/10.01.20.

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Face detection technologies are used in a large variety of applications like advertising, entertainment, video coding, digital cameras, CCTV surveillance and even in military use. It is especially crucial in face recognition systems. You can’t recognise faces that you can’t detect, right? But a single face detection algorithm won’t work in the same way in every situation. It all comes down to how the algorithm works. For example, the Kanade-Lucas-Tomasi algorithm makes use of spatial common intensity transformation to direct the deep search for the position that shows the best match. It is much faster than other traditional techniques for checking far fewer potential matches between pictures. Similarly, another common face detection algorithm is the Viola-Jones algorithm that is the most widely used face detection algorithm. It is used in most digital cameras and mobile phones to detect faces. It uses cascades to detect edges like the nose, the ears etc. However, if there is a group of people and their faces are close to each other, the algorithm might not work that well as edges tend to overlap in a crowd. It might not detect individual faces. Therefore, in this work, we test both the Viola-Jones and the Kanade-Lucas-Tomasi algorithm for each image to find out which algorithm works best in which scenario.

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Liu Xinyu, 刘昕宇, 闫铮 Yan Zheng, 段放 Duan Fang, and 戴中颖 Dai Zhongying. "Tracking of Human Respiratory Motion Based on Kanade-Lucas-Tomasi Algorithm." Laser & Optoelectronics Progress 57, no. 22 (2020): 221001. http://dx.doi.org/10.3788/lop57.221001.

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Wang, Lu, Yafeng Zhang, Xiaohong Lin, and Zheng Yan. "Study of lumbar spine activity regularity based on Kanade-Lucas-Tomasi algorithm." Biomedical Signal Processing and Control 49 (March 2019): 465–72. http://dx.doi.org/10.1016/j.bspc.2018.12.023.

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4

Mstafa, Ramadhan J., and Khaled M. Elleithy. "A video steganography algorithm based on Kanade-Lucas-Tomasi tracking algorithm and error correcting codes." Multimedia Tools and Applications 75, no. 17 (2015): 10311–33. http://dx.doi.org/10.1007/s11042-015-3060-0.

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Belmont, Barry, Ross Kessler, Nikhil Theyyunni, et al. "Continuous Inferior Vena Cava Diameter Tracking through an Iterative Kanade–Lucas–Tomasi-Based Algorithm." Ultrasound in Medicine & Biology 44, no. 12 (2018): 2793–801. http://dx.doi.org/10.1016/j.ultrasmedbio.2018.07.022.

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6

Liu, Yuan Min, and Lian Fang Tian. "An Improved Algorithm on Adaptive KLT Vision Tracking." Advanced Materials Research 631-632 (January 2013): 1270–75. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.1270.

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In view of the shortage of the KLT (Kanade-Lucas-Tomasi) tracking algorithm, an improved adaptive tracking method based on KLT is proposed in this paper, in which a kind of filtering mechanism is applied to decrease the effects of noise and illumination on tracking system. In order to eliminate the error of tracking, the strategies based on forward-backward error and measurement validity are utilized properly. However, because the approach to forward-backward error makes the feature points reduce, which leads to tracking failure especially when the shapes of object change, a method for appending the feature points is introduced. Experimental results indicate that the method presented in this paper is state of the art robustness in our comparison with related work and demonstrate improved generalization over the conventional methods.

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7

Lailia Rahmawati. "Deteksi Abnormality Dengan Menggunakan Algortima KLT Pada Citra Video CCTV." Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan 8, no. 2 (2017): 67–72. http://dx.doi.org/10.48056/jintake.v8i2.27.

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Abnormality Detection on CCTV video images that are shown on a particular individual or a particular object where the movement is captured by a minority. The use of KLT Algorithm (Kanade-Lucas-Tomasi) as Algortima Tracing results of a video that has been done Residual Spectral process that aims to produce a global movement that allocates an interesting area. From the comparison results it is known that the number of pixels in the detection move smaller than the detection is not moving. Then the result is that, the abnormal minority is that there is an object moving on its own compared to the other objects

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8

Kim, Hyeon, Jaechan Cho, Yongchul Jung, Seongjoo Lee, and Yunho Jung. "Area-Efficient Vision-Based Feature Tracker for Autonomous Hovering of Unmanned Aerial Vehicle." Electronics 9, no. 10 (2020): 1591. http://dx.doi.org/10.3390/electronics9101591.

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In this paper, we propose a vision-based feature tracker for the autonomous hovering of an unmanned aerial vehicle (UAV) and present an area-efficient hardware architecture for its integration into a flight control system-on-chip, which is essential for small UAVs. The proposed feature tracker is based on the Shi–Tomasi algorithm for feature detection and the pyramidal Lucas–Kanade (PLK) algorithm for feature tracking. By applying an efficient hardware structure that leverages the common computations between the Shi–Tomasi and PLK algorithms, the proposed feature tracker offers good tracking performance with fewer hardware resources than existing feature tracker implementations. To evaluate the tracking performance of the proposed feature tracker, we compared it with the GPS-based trajectories of a drone in various flight environments, such as lawn, asphalt, and sidewalk blocks. The proposed tracker exhibited an average accuracy of 0.039 in terms of normalized root-mean-square error (NRMSE). The proposed feature tracker was designed using the Verilog hardware description language and implemented on a field-programmable gate array (FPGA). The proposed feature tracker has 2744 slices, 25 DSPs, and 93 Kbit memory and can support the real-time processing at 417 FPS and an operating frequency of 130 MHz for 640 × 480 VGA images.

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9

Hsieh, Chung-Hung, and Jiann-Der Lee. "Markerless Augmented Reality via Stereo Video See-Through Head-Mounted Display Device." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/329415.

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Conventionally, the camera localization for augmented reality (AR) relies on detecting a known pattern within the captured images. In this study, a markerless AR scheme has been designed based on a Stereo Video See-Through Head-Mounted Display (HMD) device. The proposed markerless AR scheme can be utilized for medical applications such as training, telementoring, or preoperative explanation. Firstly, a virtual model for AR visualization is aligned to the target in physical space by an improved Iterative Closest Point (ICP) based surface registration algorithm, with the target surface structure reconstructed by a stereo camera pair; then, a markerless AR camera localization method is designed based on the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm and the Random Sample Consensus (RANSAC) correction algorithm. Our AR camera localization method is shown to be better than the traditional marker-based and sensor-based AR environment. The demonstration system was evaluated with a plastic dummy head and the display result is satisfactory for a multiple-view observation.

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Chermak, L., N. Aouf, and M. A. Richardson. "Scale robust IMU-assisted KLT for stereo visual odometry solution." Robotica 35, no. 9 (2016): 1864–87. http://dx.doi.org/10.1017/s0263574716000552.

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SUMMARYWe propose a novel stereo visual IMU-assisted (Inertial Measurement Unit) technique that extends to large inter-frame motion the use of KLT tracker (Kanade–Lucas–Tomasi). The constrained and coherent inter-frame motion acquired from the IMU is applied to detected features through homogenous transform using 3D geometry and stereoscopy properties. This predicts efficiently the projection of the optical flow in subsequent images. Accurate adaptive tracking windows limit tracking areas resulting in a minimum of lost features and also prevent tracking of dynamic objects. This new feature tracking approach is adopted as part of a fast and robust visual odometry algorithm based on double dogleg trust region method. Comparisons with gyro-aided KLT and variants approaches show that our technique is able to maintain minimum loss of features and low computational cost even on image sequences presenting important scale change. Visual odometry solution based on this IMU-assisted KLT gives more accurate result than INS/GPS solution for trajectory generation in certain context.

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