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1
Supriyatin, Wahyu. "ANALISIS PERBANDINGAN PELACAKAN OBJEK MENGGUNAKAN ALGORITMA HORN-SCHUNCK DAN LUCAS-KANADE." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 17, no. 2 (July 14, 2020): 362–71. http://dx.doi.org/10.33751/komputasi.v17i2.2146.
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Object tracking one of computer vision. Computer vision similar to human eye function. The difficulty is to detect presence an object and object tracking application made. Object tracking used in aircraft, track cars, human body detectors at airports, a regulator the number of vehicles pass and navigation tools on robots. This study is to identify objects that pass in frame. This research also count the number of objects that pass in one frame. Object tracking done by comparing two algorithms namely Horn-Schunck and Lucas-Kanade. Both algorithms tested using the Source Block Parameter and Function Block Parameter. The test carried out with video resolution 120x160 and the position camera is 2-4 m. The object tracking test is conducted in the duration of 110-120 seconds. Stages tracking object was thresholding, filtering and region successfully obtain object binary video. The Lucas-Kanade has faster in identifying objects compared to the Horn-Schunck algorithm.
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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 (March 9, 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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Hidayat, Nanda Maulana. "Aplikasi Augmented Reality Perlengkapan Militer Menggunakan Algoritma FAST Corner Dan Lucas Kanade." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 8, no. 3 (September 14, 2021): 1417–28. http://dx.doi.org/10.35957/jatisi.v8i3.1060.
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Pengetahuan mengenai perlengkapan kemiliteran tidak hanya diperoleh melalui pameran saja, dengan adanya perkembangan teknologi informasi bisa diperoleh melalui aplikasi. Penggunaan aplikasi augmented reality dapat dijadikan contoh dalam pengetahuan perlengkapan militer secara tiga dimensi. Perancangan ini bertujuan memberikan pengetahuan kepada masyarakat umum dengan menggunakan teknologi Augmented Reality. Perancangan ini menggunakan metode ADDIE dan algoritma FAST Corner dan Lucas Kanade. Perancangan ini menghasilkan sebuah aplikasi yang berisi pengenalan perlengkapan militer yang terdiri dari baret, topi boonie, topi patroli, helm, kacamata, tas besar, P3K (Pertolongan Pertama Pada kecelakaan), pisau, senjata jauh dan senjata menengah. Informasi singkat mengenai masing-masing perlengkapan militer yang dirancang secara menarik agar masyarakat umum mudah memahami dalam menggunakan aplikasi yang dapat dijalankan pada platform Android.
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Li, Ling, Zhengwei He, Sheng Chen, Xiongfa Mai, Asi Zhang, Baoqing Hu, Zhi Li, and Xinhua Tong. "Subpixel-Based Precipitation Nowcasting with the Pyramid Lucas–Kanade Optical Flow Technique." Atmosphere 9, no. 7 (July 12, 2018): 260. http://dx.doi.org/10.3390/atmos9070260.
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Short-term high-resolution quantitative precipitation forecasting (QPF) is very important for flash-flood warning, navigation safety, and other hydrological applications. This paper proposes a subpixel-based QPF algorithm using a pyramid Lucas–Kanade optical flow technique (SPLK) for short-time rainfall forecast. The SPLK tracks the storm on the subpixel level by using the optical flow technique and then extrapolates the precipitation using a linear method through redistribution and interpolation. The SPLK compares with object-based and pixel-based nowcasting algorithms using eight thunderstorm events to assess its performance. The results suggest that the SPLK can perform better nowcasting of precipitation than the object-based and pixel-based algorithms with higher adequacy in tracking and predicting severe storms in 0–2 h lead-time forecasting.
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Zhang, Xiaoli, Punan Li, and Yibing Li. "Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas–Kanade Algorithm." Journal of Healthcare Engineering 2021 (August 3, 2021): 1–10. http://dx.doi.org/10.1155/2021/4959727.
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The purpose of this research is to study the application effect of Lucas–Kanade algorithm in right ventricular color Doppler ultrasound feature point extraction and motion tracking under the condition of scale invariant feature transform (SIFT). This study took the right ventricle as an example to analyze the extraction effect and calculation rate of SIFT algorithm and improved Lucas–Kanade algorithm. It was found that the calculation time before and after noise removal by the SIFT algorithm was 0.49 s and 0.46 s, respectively, and the number of extracted feature points was 703 and 698, respectively. The number of feature points extracted by the SIFT algorithm and the calculation time were significantly better than those of other algorithms ( P < 0.01 ). The mean logarithm of the matching points of the SIFT algorithm for order matching and reverse order matching was 20.54 and 20.46, respectively. The calculation time and the number of feature points for the SIFT speckle tracking method were 1198.85 s and 81, respectively, and those of the optical flow method were 3274.19 s and 80, respectively. The calculation time of the SIFT speckle tracking method was significantly lower than that of the optical flow method ( P < 0.05 ), and there was no statistical difference in the number of feature points between the SIFT speckle tracking method and the optical flow method ( P > 0.05 ). In conclusion, the improved Lucas–Kanade algorithm based on SIFT significantly improves the accuracy of feature extraction and motion tracking of color Doppler ultrasound, which shows the value of the algorithm in the clinical application of color Doppler ultrasound.
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EVANGELIDIS, GEORGIOS D., and EMMANOUIL Z. PSARAKIS. "AN ECC-BASED ITERATIVE ALGORITHM FOR PHOTOMETRIC INVARIANT PROJECTIVE REGISTRATION." International Journal on Artificial Intelligence Tools 18, no. 01 (February 2009): 121–39. http://dx.doi.org/10.1142/s021821300900007x.
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The ability of an algorithm to accurately estimate the parameters of the geometric transformation which aligns two image profiles even in the presence of photometric distortions can be considered as a basic requirement in many computer vision applications. Projective transformations constitute a general class which includes as special cases the affine, as well as the metric subclasses of transformations. In this paper the applicability of a recently proposed iterative algorithm, which uses the Enhanced Correlation Coefficient as a performance criterion, in the projective image registration problem is investigated. The main theoretical results concerning the proposed iterative algorithm are presented. Furthermore, the performance of the iterative algorithm in the presence of nonlinear photometric distortions is compared against the leading Lucas-Kanade algorithm and its simultaneous inverse compositional variant with the help of a series of experiments involving strong or weak geometric deformations, ideal and noisy conditions and even over-modelling of the warping process. Although under ideal conditions the proposed algorithm and simultaneous inverse compositional algorithm exhibit a similar performance and both outperform the Lucas-Kanade algorithm, under noisy conditions the proposed algorithm outperforms the other algorithms in convergence speed and accuracy, and exhibits robustness against photometric distortions.
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Lin, Yih-Lon. "Least Trimmed Squares Approach to Lucas-Kanade Algorithm in Object Tracking Problems." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/324824.
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The object tracking problem is an important research topic in computer vision. For real applications such as vehicle tracking and face tracking, there are many efficient and real-time algorithms. In this study, we will focus on the Lucas-Kanade (LK) algorithm for object tracking. Although this method is time consuming, it is effective in tracking accuracy and environment adaptation. In the standard LK method, the sum of squared errors is used as the cost function, while least trimmed squares is adopted as the cost function in this study. The resulting estimator is robust against outliers caused by noises and occlusions in the tracking process. Simulations are provided to show that the proposed algorithm outperforms the standard LK method in the sense that it is robust against the outliers in the object tracking problems.
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Rahmawati, Lailia. "Deteksi Abnormality Dengan Menggunakan Algortima KLT Pada Citra Video CCTV." Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan 8, no. 2 (October 10, 2017): 67–72. http://dx.doi.org/10.32492/jintake.v8i2.693.
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Deteksi Abnormality pada citra video CCTV yang ditunjukan pada individu tertentu atau suatu objek tertentu dimana gerakan diangkap minoritas. Penggunaan Algoritma KLT (Kanade-Lucas-Tomasi) sebagai Algortima Tracing hasil video yang sudah dilakukan proses Spektral Residual yang bertujuan menghasilkan gerakan global yang mengalokasikan daerah yang menarik. Dari hasil perbandingan tersebut diketahui bahwa jumlah piksel pada deteksi bergerak lebih kecil dari deteksi yang tidak bergerak. Maka hasilnya adalah yang ,minoritas abnormal adalah ada objek yang bergerak sendiri dibandingkan dengan objek-objek yang lainnya
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Wang, Yan Ni. "A Local Optical Flow Constraint Target Extracting Algorithm of Kalman Filter Based on Background Modeling." Applied Mechanics and Materials 596 (July 2014): 316–21. http://dx.doi.org/10.4028/www.scientific.net/amm.596.316.
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Owing to the problems of inter frame difference method cannot extract the entire target and most optical flow algorithms with computational time, poor real-time performance, a local optical flow constraint target extracting algorithm of Kalman filter based on background modeling is proposed. Firstly use Kalman filter method based on background modeling predict and update the background, then make Lucas-Kanade local optical flow algorithm search the background changing region, finally determine the gray contour, extract target. Compared with the classical algorithms, the simulation results show that new algorithm can extract the moving object quickly and accurately, and has better robustness to the environment changes and target circuitous movement.
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Shen, Lu Rong, Xia Bin Dong, Rui Tao Lu, Yong Bin Zheng, and Xin Sheng Huang. "Robust Spatial-Color Feature with New Similarity Measure and Adaptive Template Update for Mean-Shift Tracking." Applied Mechanics and Materials 321-324 (June 2013): 1021–29. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1021.
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In this paper, we analyze the object tracking task of mean-shift algorithm. A spatial-color and similarity based mean-shift tracking algorithm is proposed. The spatial-color feature is used to replace the color histogram, and an enhanced algorithm is derived by adopting a new similarity measure. We also introduce Lucas-Kanade algorithm to design a template update strategy, propose a template update algorithm for mean-shift. Experimental results show that these two improved mean-shift tracking algorithms have high tracking accuracy and good robustness to the change of appearance of the object.
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Premaratne, Prashan, Sabooh Ajaz, and Malin Premaratne. "Hand gesture tracking and recognition system using Lucas–Kanade algorithms for control of consumer electronics." Neurocomputing 116 (September 2013): 242–49. http://dx.doi.org/10.1016/j.neucom.2011.11.039.
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Tong, W. "Formulation of Lucas-Kanade Digital Image Correlation Algorithms for Non-contact Deformation Measurements: A Review." Strain 49, no. 4 (July 15, 2013): 313–34. http://dx.doi.org/10.1111/str.12039.
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Yusuf, Muhammad, Fauziah Fauziah, and Aris Gunaryati. "TEKNOLOGI MIXED REALITY PADA APLIKASI TUNTUNAN SHALAT MAGHRIB MENGGUNAKAN ALGORITMA FAST CORNER DETECTION DAN LUCAS KANADE." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 6, no. 1 (May 31, 2021): 82–93. http://dx.doi.org/10.29100/jipi.v6i1.1905.
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Ammar, Anis, Hana Ben Fredj, and Chokri Souani. "Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System." Electronics 10, no. 17 (September 4, 2021): 2164. http://dx.doi.org/10.3390/electronics10172164.
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Motion estimation has become one of the most important techniques used in realtime computer vision application. There are several algorithms to estimate object motions. One of the most widespread techniques consists of calculating the apparent velocity field observed between two successive images of the same scene, known as the optical flow. However, the high accuracy of dense optical flow estimation is costly in run time. In this context, we designed an accurate motion estimation system based on the calculation of the optical flow of a moving object using the Lucas–Kanade algorithm. Our approach was applied on a local treatment region implemented into Raspberry Pi 4, with several improvements. The efficiency of our accurate realtime implementation was demonstrated by the experimental results, showing better performance than with the conventional calculation.
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M., Murugappan, and Mutawa A. "Facial geometric feature extraction based emotional expression classification using machine learning algorithms." PLOS ONE 16, no. 2 (February 18, 2021): e0247131. http://dx.doi.org/10.1371/journal.pone.0247131.
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Emotion plays a significant role in interpersonal communication and also improving social life. In recent years, facial emotion recognition is highly adopted in developing human-computer interfaces (HCI) and humanoid robots. In this work, a triangulation method for extracting a novel set of geometric features is proposed to classify six emotional expressions (sadness, anger, fear, surprise, disgust, and happiness) using computer-generated markers. The subject’s face is recognized by using Haar-like features. A mathematical model has been applied to positions of eight virtual markers in a defined location on the subject’s face in an automated way. Five triangles are formed by manipulating eight markers’ positions as an edge of each triangle. Later, these eight markers are uninterruptedly tracked by Lucas- Kanade optical flow algorithm while subjects’ articulating facial expressions. The movement of the markers during facial expression directly changes the property of each triangle. The area of the triangle (AoT), Inscribed circle circumference (ICC), and the Inscribed circle area of a triangle (ICAT) are extracted as features to classify the facial emotions. These features are used to distinguish six different facial emotions using various types of machine learning algorithms. The inscribed circle area of the triangle (ICAT) feature gives a maximum mean classification rate of 98.17% using a Random Forest (RF) classifier compared to other features and classifiers in distinguishing emotional expressions.
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Deng, Zilong, Dongxiao Yang, Xiaohu Zhang, Yuguang Dong, Chengbo Liu, and Qiang Shen. "Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching." Electronics 9, no. 1 (January 20, 2020): 198. http://dx.doi.org/10.3390/electronics9010198.
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The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms.
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Arokiasami, Willson Amalraj, Prahlad Vadakkepat, Kay Chen Tan, and Dipti Srinivasan. "Real-Time Path-Generation and Path-Following Using an Interoperable Multi-Agent Framework." Unmanned Systems 06, no. 04 (October 2018): 231–50. http://dx.doi.org/10.1142/s2301385018500061.
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Autonomous unmanned vehicles are preferable in patrolling, surveillance and, search and rescue missions. Multi-agent architectures are commonly used for autonomous control of unmanned vehicles. Existing multi-robot architectures for unmanned aerial and ground robots are generally mission and platform oriented. Collision avoidance, path-planning and tracking are some of the fundamental requirements for autonomous operation of unmanned robots. Though aerial and ground vehicles operate differently, the algorithms for obstacle avoidance, path-planning and path-tracking can be generalized. Service Oriented Interoperable Framework for Robot Autonomy (SOIFRA) proposed in this work is an interoperable multi-agent framework focused on generalizing platform independent algorithms for unmanned aerial and ground vehicles. SOIFRA is behavior-based, modular and interoperable across unmanned aerial and ground vehicles. SOIFRA provides collision avoidance, and, path-planning and tracking behaviors for unmanned aerial and ground vehicles. Vector Directed Path-Generation and Tracking (VDPGT), a vector-based algorithm for real-time path-generation and tracking, is proposed in this work. VDPGT dynamically adopts the shortest path to the destination while minimizing the tracking error. Collision avoidance is performed utilizing Hough transform, Canny contour, Lucas–Kanade sparse optical flow algorithm and expansion of object-based time-to-contact estimation. Simulation and experimental results from Turtlebot and AR Drone show that VDPGT can dynamically generate and track paths, and SOIFRA is interoperable across multiple robotic platforms.
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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 (September 28, 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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Mahmoudi, S. A., M. Kierzynka, P. Manneback, and K. Kurowski. "Real-time motion tracking using optical flow on multiple GPUs." Bulletin of the Polish Academy of Sciences: Technical Sciences 62, no. 1 (March 1, 2014): 139–50. http://dx.doi.org/10.2478/bpasts-2014-0016.
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Abstract Motion tracking algorithms are widely used in computer vision related research. However, the new video standards, especially those in high resolutions, cause that current implementations, even running on modern hardware, no longer meet the needs of real-time processing. To overcome this challenge several GPU (Graphics Processing Unit) computing approaches have recently been proposed. Although they present a great potential of a GPU platform, hardly any is able to process high definition video sequences efficiently. Thus, a need arose to develop a tool being able to address the outlined problem. In this paper we present software that implements optical flow motion tracking using the Lucas-Kanade algorithm. It is also integrated with the Harris corner detector and therefore the algorithm may perform sparse tracking, i.e. tracking of the meaningful pixels only. This allows to substantially lower the computational burden of the method. Moreover, both parts of the algorithm, i.e. corner selection and tracking, are implemented on GPU and, as a result, the software is immensely fast, allowing for real-time motion tracking on videos in Full HD or even 4K format. In order to deliver the highest performance, it also supports multiple GPU systems, where it scales up very well
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Alsbou, Nesreen, Salahuddin Ahmad, and Imad Ali. "Correlation of displacement vector fields calculated by different deformable image registration algorithms with motion parameters in helical, axial and cone beam CT imaging." Journal of Radiotherapy in Practice 19, no. 3 (September 16, 2019): 219–25. http://dx.doi.org/10.1017/s1460396919000657.
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AbstractAim:The purpose of this study is to investigate quantitatively the correlation of displacement vector fields (DVFs) from different deformable image registration (DIR) algorithms to register images from helical computed tomography (HCT), axial computed tomography (ACT) and cone beam computed tomography (CBCT) with motion parameters.Materials and methods:CT images obtained from scanning of the mobile phantom were registered with the stationary CT images using four DIR algorithms from the DIRART software: Demons, Fast-Demons, Horn–Schunck and Lucas–Kanade. HCT, ACT and CBCT imaging techniques were used to image a mobile phantom, which included three targets with different sizes (small, medium and large) that were manufactured from a water-equivalent material and embedded in low-density foam to simulate lung lesions. The phantom was moved with controlled cyclic motion patterns where a range of motion amplitudes (0–20 mm) and frequencies (0·125–0·5 Hz) were used.Results:The DVF obtained from different algorithms correlated well with motion amplitudes applied on the mobile phantom for CBCT and HCT, where the maximal DVF increased linearly with the motion amplitudes of the mobile phantom. In ACT, the DVF correlated less with motion amplitudes where motion-induced strong image artefacts and the DIR algorithms were not able to deform the ACT image of the mobile targets to the stationary targets. Three DIR algorithms produce comparable values and patterns of the DVF for certain CT imaging modality. However, DVF from Fast-Demons deviated strongly from other algorithms at large motion amplitudes.Conclusions:The local DVFs provide direct quantitative values for the actual internal tumour shifts that can be used to determine margins for the internal target volume that consider tumour motion during treatment planning. Furthermore, the DVF distributions can be used to extract motion parameters such as motion amplitude that can be extracted from the maximal or minimal DVF calculated by the different DIR algorithms and used in the management of the patient motion.
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Valappil, Najiya K., and Qurban A. Memon. "CNN-SVM based vehicle detection for UAV platform." International Journal of Hybrid Intelligent Systems 17, no. 1-2 (July 13, 2021): 59–70. http://dx.doi.org/10.3233/his-210003.
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Conventional surveillance devices are deployed at fixed locations on road sideways, poles or on traffic lights, which provide a constant and fixed surveillance view of the urban traffic. Unmanned aerial vehicles (UAVs) have for last two decades received considerable attention in building smart and effective system with wider coverage using low cost, highly flexible unmanned platform for smart city infrastructure. Unlike fixed monitoring devices, the camera platform of aerial vehicles has many constraints, as it is in constant motion including titling and panning, and thus makes it difficult to process data for real time applications. The inaccuracy in object detection rates from UAV videos has motivated the research community to combine different approaches such as optical flow and supervised learning algorithms. The method proposed in this research incorporates steps that include Kanade-Lucas optical flow method for moving object detection, building connected graphs to isolate objects and convolutional neural network (CNN), followed by support vector machine (SVM) for final classification. The generated optical flow contains background (and tiny) objects detected as vehicle as the camera platform moves. The classifier introduced here rules out the presence of any other (moving) objects to be detected as vehicles. The methodology adopted is tested on a stationary and moving aerial videos. The system is shown to have performance accuracy of 100% in case of stationary video and 98% in case of video from aerial platform.
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Pirnar, Žan, Miha Finžgar, and Primož Podržaj. "Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step." Applied Sciences 11, no. 8 (April 13, 2021): 3467. http://dx.doi.org/10.3390/app11083467.
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Traditionally, the first step in physiological measurements based on remote photoplethysmography (rPPG) is localizing the region of interest (ROI) that contains a desired pulsatile information. Recently, approaches that do not require this step have been proposed. The purpose of this study was to evaluate the performance of selected approaches with and without ROI localization step in rPPG signal extraction. The Viola-Jones face detector and Kanade–Lucas–Tomasi tracker (VK) in combination with (a) a region-of-interest (ROI) cropping, (b) facial landmarks, (c) skin-color segmentation, and (d) skin detection based on maximization of mutual information and an approach without ROI localization step (Full Video Pulse (FVP)) were studied. Final rPPG signals were extracted using selected model-based and data-driven rPPG algorithms. The performance of the approaches was tested on three publicly available data sets offering compressed and uncompressed video recordings covering various scenarios. The success rates of pulse waveform signal extraction range from 88.37% (VK with skin-color segmentation) to 100% (FVP). In challenging scenarios (skin tone, lighting conditions, exercise), there were no statistically significant differences between the studied approaches in terms of SNR. The best overall performance in terms of RMSE was achieved by a combination of VK with ROI cropping and the model-based rPPG algorithm. Results indicate that the selection of the ROI localization approach does not significantly affect rPPG measurements if combined with a robust algorithm for rPPG signal extraction.
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Pearce, Sophie, Robert Ljubičić, Salvador Peña-Haro, Matthew Perks, Flavia Tauro, Alonso Pizarro, Silvano Dal Sasso, et al. "An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems." Remote Sensing 12, no. 2 (January 9, 2020): 232. http://dx.doi.org/10.3390/rs12020232.
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Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s − 1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s − 1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s − 1 of the ADCP measurements, on average.
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Zhou, Bin, and Min Chen. "MRI Images under the Optimized Registration Algorithm for Primary Open Angle Glaucoma Visual Path Damage." Scientific Programming 2021 (July 5, 2021): 1–9. http://dx.doi.org/10.1155/2021/4921276.
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To explore the impact of different image registration algorithms on the diagnosis of visual path damage in patients with primary open angle glaucoma (POAG), 60 cases of suspected POAG patients were selected as the research objects. Shape-preserving scale invariant feature transform (SP-SIFT) algorithm, scale invariant feature transform (SIFT) algorithm, and Kanade-Lucas-Tomasi (KLT) algorithm were compared and applied to MRI images of 60 POAG patients. It was found that the SP-SIFT algorithm converged after 33 iterations, which had a higher registration speed than the SIFT algorithm and the KLT algorithm. The mean errors of the SP-SIFT algorithm in the rotation angle, X-direction translation, and Y-direction translation were 2.11%, 4.56%, and 4.31%, respectively. Those of the SIFT algorithm were 5.55%, 9.98%, and 7.01%, respectively. Those of the KLT algorithm were 7.45%, 11.31%, and 8.56%, respectively, and the difference among algorithms was significant ( P < 0.05 ). The diagnostic sensitivity and accuracy of the SP-SIFT algorithm for POAG were 96.15% and 94.34%, respectively. Those of the SIFT algorithm were 94.68% and 90.74%, respectively. Those of the KLT algorithm were 94.21% and 90.57%, respectively, and the three algorithms had significant differences ( P < 0.05 ). The results of MRI images based on the SP-SIFT algorithm showed that the average thickness of the cortex of the patient’s left talar sulcus, right talar sulcus, left middle temporal gyrus, and left fusiform gyrus were 2.49 ± 0.15 mm, 2.62 ± 0.13 mm, 3.00 ± 0.10 mm, and 2.99 ± 0.17 mm, respectively. Those of the SIFT algorithm were 2.51 ± 0.17 mm, 2.69 ± 0.12 mm, 3.11 ± 0.13 mm, and 3.09 ± 0.14 mm, respectively. Those of the KLT algorithm were 2.35 ± 0.12 mm, 2.52 ± 0.16 mm, 2.77 ± 0.11 mm, and 2.87 ± 0.17 mm, respectively, and the three algorithms had significant differences ( P < 0.05 ). In summary, the SP-SIFT algorithm was ideal for POAG visual pathway diagnosis and was of great adoption potential in clinical diagnosis.
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Ramkumar, E., T. Guna, S. M. Dharshan, and V. S. Ashok Ramanan. "Implementing Facial Recognition by Interfacing MATLAB Along with Arduino." International Journal of Innovative Technology and Exploring Engineering 10, no. 4 (February 28, 2021): 66–71. http://dx.doi.org/10.35940/ijitee.d8478.0210421.
Full textAbstract:
Facial recognition has become one of the recent trends in attracting abundant attention within the society of social media network. The face is flat and therefore needs plenty of mathematical computations. Facial knowledge has become one in every of the foremost necessary biometric, we tend to witness it from the day-to-day gadgets like mobile phones. Every transportable electronic device currently being discharged includes a camera embedded in it. Network access management via face recognition not solely makes hackers just about not possible to steal one's "password", however conjointly will increase the user-friendliness in human-computer interaction. For the applications of videophone and conference, the help of face recognition conjointly provides an additional economical secret writing theme. Face detection technologies are employed in an oversized kind of applications like advertising, diversion, video secret writing, digital cameras, CCTV police investigation, and even in military use. Totally different algorithms are used for biometric authentication. The Kanade-Lucas-Tomasi rule makes use of abstraction common intensity transformation to direct the deep explore for the position that shows the simplest match. Another common face detection rule is that the Viola-Jones rule that's the foremost wide used face detection rule. It's employed in most digital cameras and mobile phones to notice faces. It uses cascades to notice edges just like the nose, the ears, etc. Hence, during this paper, we've got planned the Viola-Jones rule because the best one supported our application. The rule is employed within the biometric authentication of individuals and also the pictures are kept during processing. The kept information is employed for recognizing the faces and if the information matches, an impression signal is given to the controller. The MATLAB software is employed to relinquish control signals to the motor, which is employed for gap and shutting the door. The input image is fed by a digital camera and also the image is processed within MATLAB. The output is given to the external controller interfaced with MATLAB. The image process field has several sub-fields, biometric authentication is one in each of them because it gains additional quality for security functions these days. The planned system can be employed in residential buildings, malls, and industrial sectors. Thus, this technique is helpful for homemakers to be safer in their homes.
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Romero, Juan, Damien Verdier, Clement Raffaitin, Luis Miguel Procel, and Lionel Trojman. "Simple Hardware Implementation of Motion Estimation Algorithms." ACI Avances en Ciencias e Ingenierías 11, no. 3 (September 25, 2019). http://dx.doi.org/10.18272/aci.v11i3.1352.
Full textAbstract:
We present in the following work a hardware implementation of the two principal optical flow methods. The work is based on the methods developed by Lucas & Kanade, and Horn & Schunck. The implementation is made by using a field programmable gate array and Hardware Description Language. To achieve a successful implementation, the algorithms were optimized. The results show the optical flow as a vector field over one frame, which enable an easy detection of the movement. The results are compared to a software implementation to insure the success of the method. The implementation is a fast implementation capable of quickly overcoming a traditional implementation in software.
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Morlier, Joseph, and Guilhem Michon. "Virtual Vibration Measurement Using KLT Motion Tracking Algorithm." Journal of Dynamic Systems, Measurement, and Control 132, no. 1 (December 1, 2009). http://dx.doi.org/10.1115/1.4000070.
Full textAbstract:
This paper presents a practical framework and its applications of motion tracking algorithms applied to structural dynamics. Tracking points (“features”) across multiple images are a fundamental operation in many computer vision applications. The aim of this work is to show the capability of computer vision (CV) for estimating the dynamic characteristics of two mechanical systems using a noncontact, markerless, and simultaneous single input multiple output analysis. Kanade–Lucas–Tomasi trackers are used as virtual sensors on mechanical systems’ video from a high speed camera. First we introduce the paradigm of virtual sensors in the field of modal analysis using video processing. To validate our method, a simple experiment is proposed: an Oberst beam test with harmonic excitation (mode 1). Then with the example of a helicopter blade, frequency response functions’ (FRFs) reconstruction is carried out by introducing several signal processing enhancements (filtering and smoothing). The CV experimental results (frequencies and mode shapes) are compared with the classical modal approach and the finite element model (FEM) showing high correlation. The main interest of this method is that displacements are simply measured using only video at fps respecting the Nyquist frequency.