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Improvement of a face recognition method for high jumper with a single sample based on lucas-kanade algorithm

INTERNATIONAL JOURNAL OF BIOMETRICS 2021年第2-3期13卷 258-271页

作者： Shi, Guoxing Wuxi Inst Technol Wuxi 214000 Jiangsu Peoples R China

In order to improve the identification accuracy of a dynamic single sample, a face recognition method based on lucas-kanade algorithm is proposed. The weighted lucas-kanade (LK) algorithm is used to obtain the single-sample affine transformation parameters of the high jumper's side face block and the corresponding front face block, and the optimal parameters of face pose correction are found through the maximum Gabor similarity, the method of face recognition for high jumper with a single sample is completed. Simulation results show that both the front face recognition rate and side-face recognition rate of the proposed method can reach more than 95% and the face recognition recall rate of the proposed method ranges from 90% to 100%. Compared with the traditional method, the recall rate has been significantly improved. In addition, when there are 440 face images, the recognition time is 1,177 ms, which is shorter than the traditional method.

关键词： lucas-kanade algorithm high jumper with a single sample face recognition method improvement

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Real-Time Affine Tracking Using Re-Located lucas-kanade algorithm 3

Real-Time Affine Tracking Using Re-Located Lucas-Kanade Algo...

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International Conference on Robot Vision and Signal Processing

作者： Ma, Xiao Fan, Nana He, Zhenyu Yang, Wei-Guo Harbin Inst Technol Shenzhen Grad Sch Sch Comp Sci & Technol Shenzhen Peoples R China Shenzhen Konka Commun Technol Co Shenzhen Peoples R China

ISBN: (纸本)9781467396462

Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers are lack of an ability to adapt affine transforms of objects. The lucas-kanade(L-K) algorithm is a classic gradient decent search method which has been widely applied in template matching, image alignment and visual tracking. Despite the L-K algorithm has a good property on adapting targets affine transforms, it always falls into a local optimal since the background information is accumulatively introduced to the template. In this paper, we propose a novel approach, called the Re-located L-K algorithm(RL-K), to handle affine transforms of objects. In particular, we utilize the Multiple Channel Correlation Filters(MMCFs) to deal with the translations of the object before the L-K's gradient decent search step. In addition, we integrate the gradient orientation features into the L-K algorithm to improve its robustness to visual tracking. The experimental results show that, comparing with the state-of-the-art affine tracking approaches, our approach is running at real-time without accuracy losing.

关键词： Visual tracking lucas-kanade algorithm Correlation filters

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Real-Time Non-contact Breath Detection from Video using Adaboost and lucas-kanade algorithm

Real-Time Non-contact Breath Detection from Video using Adab...

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Joint 17th World Congress of International-Fuzzy-Systems-Association / 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS)

作者： Quoc-Viet Tran Shun-Feng Su Chen-Chia Chuang Van-Truong Nguyen Ngoc-Quan Nguyen Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan Natl Ilan Univ Dept Elect Engn Ilan Taiwan Hanoi Univ Ind Dept Mech Engn Hanoi Vietnam

ISBN: (纸本)9781509049172

A simple and economical system is developed to detect human breath in a real time fashion with a normal webcam. By using the optical flow method, a novel approach is proposed to detect the peak of inspiratory phase from a series of frames that are captured from a camera to define the time for trigging X-ray shooting. The issue of using images to detect breath is that the motions of respiratory features on chest are very small, since the camera is attached on the X-ray machine with a significant distance. Therefore, the lucas-kanade algorithm is considered to track small motions on chest. Adaboost and corner detection are developed to identify region of chest and to obtain useful feature points for the lucaskanade algorithm. From the experimental conducted, by using the proposed approach, the motion of respiratory phase is easily observed and the predictive error of the peak time is about 0.488 second, approximately 9.75% of the averaging breath cycle. It is acceptable for the X-ray shooting purpose.

关键词： Breath detection inspiratory phase respiratory phase detect respiration lucas-kanade algorithm deepest inspiration

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Dynamic displacement measurement of hoisting rope in lateral and longitudinal direction by improving lucas-kanade algorithm

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MEASUREMENT 2023年第1期218卷

作者： Zhang, Yunchang Cao, Guohua Zhu, Zhencai Li, Peiyao Wang, Gonghua China Univ Min & Technol Jiangsu Prov & Educ Minist Collaborat Innovat Ctr Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Peoples R China Yankuang Energy Grp Co Ltd Jining Peoples R China

The hoisting rope has complex dynamic characteristics under external excitation during operation, charac-terized by large longitudinal movement and minor lateral oscillation. However, there are no appropriate approaches to acquire the dynamic displacement of the rope currently. In this study, a fast vision-based measurement framework, including initial value forecast (IVF) and target tracking based on lucas-kanade (LK) algorithm, called IVF-LK, is proposed to obtain the lateral-longitudinal displacement of the rope. Firstly, an IVF method is proposed to obtain the initial value of iteration, overcoming the problem of non-convergence of LK algorithm under large displacement. Secondly, a parameter update strategy makes the convergence speed of LK significantly improved. Then, the performance of IVF-LK algorithm was tested in the experimental bench, which reveal its superior capacity in rope-structure dynamic measurement both in efficiency and precision. Finally, a field experiment was implemented to analyze the dynamic displacement of the hoisting rope.

关键词： Lateral and longitudinal displacement measurement Moving hoisting rope lucas-kanade algorithm Mine hoist

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A VLSI Architecture and algorithm for lucas-kanade-Based Optical Flow Computation

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2010年第1期18卷 29-38页

作者： Mahalingam, Venkataraman Bhattacharya, Koustav Ranganathan, Nagarajan Chakravarthula, Hari Murphy, Robin Roberson Pratt, Kevin Sheldon Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA Tessera Inc Syst Engn San Jose CA 95134 USA Texas A&M Dept Comp Sci College Stn TX 77843 USA Univ S Florida Ctr Robot Assisted Search & Rescue Tampa FL 33620 USA

Optical flow computation in vision-based systems demands substantial computational power and storage area. Hence, to enable real-time processing at high resolution, the design of application-specific system for optic flow becomes essential. In this paper, we propose an efficient VLSI architecture for the accurate computation of the lucas-kanade (L-K)-based optical flow. The L-K algorithm is first converted to a scaled fixed-point version, with optimal bit widths, for improving the feasibility of high-speed hardware implementation without much loss in accuracy. The algorithm is mapped onto an efficient VLSI architecture and the data flow exploits the principles of pipelining and parallelism. The optical flow estimation involves several tasks such as Gaussian smoothing, gradient computation, least square matrix calculation, and velocity estimation, which are processed in a pipelined fashion. The proposed architecture was simulated and verified by synthesizing onto a Xilinx Field Programmable Gate Array, which utilize less than 40% of system resources while operating at a frequency of 55 MHz. Experimental results on benchmark sequences indicate 42% improvement in accuracy and a speed up of five times, compared to a recent hardware implementation of the L-K algorithm.

关键词： Field-programmable gate array (FPGA) implementation lucas-kanade algorithm optical flow VLSI architecture

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Dynamic displacement measurement of large-scale structures based on the lucas-kanade template tracking algorithm

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2016年 66-67卷 425-436页

作者： Guo, Jie Zhu, Chang'an Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei 230027 Anhui Peoples R China

The development of optics and computer technologies enables the application of the vision-based technique that uses digital cameras to the displacement measurement of large-scale structures. Compared with traditional contact measurements, vision-based technique allows for remote measurement, has a non-intrusive characteristic, and does not necessitate mass introduction. In this study, a high-speed camera system is developed to complete the displacement measurement in real time. The system consists of a highspeed camera and a notebook computer. The high-speed camera can capture images at a speed of hundreds of frames per second. To process the captured images in computer, the lucas-kanade template tracking algorithm in the field of computer vision is introduced. Additionally, a modified inverse compositional algorithm is proposed to reduce the computing time of the original algorithm and improve the efficiency further. The modified algorithm can rapidly accomplish one displacement extraction within 1 ms without having to install any pre-designed target panel onto the structures in advance. The accuracy and the efficiency of the system in the remote measurement of dynamic displacement are demonstrated in the experiments on motion platform and sound barrier on suspension viaduct. Experimental results show that the proposed algorithm can extract accurate displacement signal and accomplish the vibration measurement of large-scale structures. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Displacement measurement High-speed camera system Template tracking lucas-kanade algorithm

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Hand gesture tracking and recognition system using lucas-kanade algorithms for control of consumer electronics

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NEUROCOMPUTING 2013年 116卷 242-249页

作者： Premaratne, Prashan Ajaz, Sabooh Premaratne, Malin Univ Wollongong Sch Elect Comp & Telecommun Engn North Wollongong NSW 2522 Australia Inha Univ Dept Informat & Commun Inchon South Korea Monash Univ Dept Elect & Comp Syst Engn Clayton Vic 3800 Australia

Dynamic hand gesture tracking and recognition system can simplify the way humans interact with computers and many other non-critical consumer electronic equipments. This system is based on the well-known "Wave Controller" technology developed at the University of Wollongong [1-3] and certainly a step forward in video gaming and consumer electronics control interfaces. Currently, computer interfacing mainly involves keyboard, mouse, joystick or gaming wheels and occasionally voice recognition for user input. These modes of interaction have constrained the artistic ability of many users, as they are required to respond to the computer through pressing buttons or moving other apparatus. Voice recognition is seen as unreliable and impractical in areas where more than one user is present. All these drawbacks can be tackled by using a reliable hand gesture tracking and recognition system based on both lucas-kanade and Moment Invariants approaches. This will facilitate interaction between users and computers and other consumer electronic equipments in real time. This will further enhance the user experience as users are no longer have any physical connection to the equipment being controlled. In this research, we have compared our proposed moment invariant based algorithm with template based and Fourier descriptor based methods to highlight the advantages and limitations of the proposed system. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Dynamic gesture recognition Support vector machines Tracking lucas-kanade algorithm Moment invariants Computer human interaction (HCI)

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A lucas-kanade moving object detection algorithm based on Gaussian mixture background modelling

A Lucas-Kanade moving object detection algorithm based on Ga...

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2014 International Conference on Civil, Urban and Environmental Engineering (CUEE2014)

作者： Y.N.Wang J.N.Zhou School of Information and Control Engineering Xi'an University of Architecture and Technology

Owing to the problems of computational time, poor real-time performance and inaccuracy of detection of inter frame difference method and most optical flow algorithms, a lucas-kanade moving object detection algorithm based on Gaussian mixture background modelling is proposed. Firstly use the Gaussian mixture modelling estimate and update the background for input reference video frames, then apply lucas-kanade optical flow constraint algorithm to calculate and compare the pixels in the area of video, and finally find the changing gray region and detect the object. Compared with similar algorithms, the simulation results show that the new algorithm can detect the moving object quickly and accurately, has a better robustness to environment changes and targets movement repeatedly.

关键词： object detection Gaussian mixture background modelling lucas-kanade algorithm local constraint

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Pyramidal lucas-kanade-Based Noncontact Breath Motion Detection

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第7期50卷 2659-2670页

作者： Tran, Quoc-Viet Su, Shun-Feng Nguyen, Van-Truong Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 106 Taiwan Natl Taiwan Univ Sci & Technol Dept Mech Engn Taipei 106 Taiwan Hanoi Univ Ind Dept Mech Engn Hanoi 159999 Vietnam

This paper aims to build a simple and low-cost system by using images to detect human breath in a real-time fashion to estimate the peak of the inspiratory phase of a breath so as to define a proper triggering timing for X-ray shooting. In fact, it is very difficult to detect very small breathing motion on images. In this paper, well-known techniques are employed to obtain useful features from the chest area for the lucas-kanade algorithm. Various levels of the Pyramidal lucas-kanade are then adapted to track possible small motions of those features. The proposed approach can successfully detect the inspiratory-expiratory motions and the peak time of inspiratory phase can be predicted within an acceptable interval of error time. From the experiments conducted, the breath motion can be successfully observed in two different environment situations (dim-lighting and lighting conditions). It can be found that the tracked features are quite robust and stable without losing the quantity over a long period of testing time. Thus, the proposed approach can effectively be used to define a proper triggering timing for X-ray shooting. Besides, in our experiments, even though the target is 6 m away, the breath detection is still successful. In other words, the proposed approach can also be used for surveillance or healthcare environments.

关键词： Mouth Radar Webcams X-ray imaging Tracking Image color analysis Breath detection lucas-kanade algorithm noncontact respiration detection

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Learning Deep lucas-kanade Siamese Network for Visual Tracking

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 4814-4827页

作者： Yao, Siyuan Han, Xiaoguang Zhang, Hua Wang, Xiao Cao, Xiaochun Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100093 Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen 518055 Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

In most recent years, Siamese trackers have drawn great attention because of their well-balanced accuracy and efficiency. Although these approaches have achieved great success, the discriminative power of the conventional Siamese trackers is still limited by the insufficient template-candidate representation. Most of the existing approaches take non-aligned features to learn a similarity function for template-candidate matching, while the target object's geometrical transformation is seldom explored. To address this problem, we propose a novel Siamese tracking framework, which enables to dynamically transform the template-candidate features to a more discriminative viewpoint for similarity matching. Specifically, we reformulate the template-candidate matching problem of the conventional Siamese tracker from the perspective of lucas-kanade (LK) image alignment approach. A lucas-kanade network (LKNet) is proposed and incorporated to the Siamese architecture to learn aligned feature representations in data-driven trainable manner, which is able to enhance the model adaptability in challenging scenarios. Within this framework, we propose two Siamese trackers named LK-Siam and LK-SiamRPN to validate the effectiveness. Extensive experiments conducted on the prevalent datasets show that the proposed method is more competitive over a number of state-of-the-art methods.

关键词： Target tracking Visualization Feature extraction Training Object tracking Task analysis Agriculture Siamese network lucas-kanade algorithm dynamic template-candidate matching object tracking

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