Gesture motion detection algorithm based on optical flow method

Gas–Liquid two-phase flows are a common flow in industrial production processes. Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L–K) optical flow method with the Split Comparison (SC) model measurement method. In the proposed approach, videos of gas–liquid two-phase flows are captured using a camera, and optical flow data are acquired from the flow videos using the pyramid L–K optical flow detection method. To address the issue of data clutter in optical flow extraction, a dynamic median value screening method is introduced to optimize the corner point for optical flow calculations. Machine learning algorithms are employed for the prediction model, yielding high flow prediction accuracy in experimental tests. Results demonstrate that the gradient boosted regression (GBR) model is the most effective among the five preset models, and the optimized SC model significantly improves measurement accuracy compared to the GBR model, achieving an R2 value of 0.97, RMSE of 0.74 m3/h, MAE of 0.52 m3/h, and MAPE of 8.0%. This method offers a new approach for monitoring flows in industrial production processes such as oil and gas.

To address the degradation of visual simultaneous localization and mapping (VSLAM) pose estimation accuracy caused by extensive moving objects and the resulting contamination of feature points in indoor dynamic environments, this study proposes a dynamic feature point rejection strategy that integrates a You Only Look Once (YOLO) family detector, YOLO11, with the Lucas–Kanade (LK) optical flow method within the ORB_SLAM3 framework. First, YOLO11 is employed to detect potentially dynamic targets such as pedestrians in the image, and ORB features extracted within the detection bounding boxes are labeled as high-risk dynamic points. Subsequently, based on pyramidal LK optical flow, the motion consistency of feature points is analyzed across multiple frames, and a secondary discrimination is performed on features located at the edges and in the vicinity of the detection boxes. In this way, feature points that conform to a static motion model are maximally preserved, thereby alleviating the excessive removal and misclassification of static points caused by oversized detection boxes. On this basis, a new map is reconstructed using the filtered static feature points, and the pose estimation process is further refined. Experiments on multiple indoor dynamic scenarios demonstrate that, compared with the original ORB_SLAM3, the proposed method effectively suppresses dynamic interference, enhances the stability of feature points, and significantly improves pose estimation accuracy and trajectory continuity, thus providing a valuable reference for robust VSLAM applications in dynamic environments.

To realize the eye pupil center location in color face image, a new algorithm based on skin color segmentation and radial symmetry transform is proposed. The algorithm makes full use of the pupil and the surrounding region’s gradient variation characteristics and radial symmetry characteristic, which can effectively solve the interference factors such as uneven spot, frame. Step: First, use the skin color model to detect the face region, and then remove the small block of the background with similar skin color by morphological opening operation and area comparison filtering method. Then, use the horizontal integral projection to determine the candidate eye regions. Finally, apply the radial symmetry transform to the candidate eye region to locate pupil center. Experimental results show that the proposed algorithm has good localization effect, and has strong robustness to various disturbances. Introduction In recent years, line of sight tracking technology is an important and active research area of computer vision. It is widely used in human-computer interaction, auxiliary driving vehicles, virtual reality and military fields [1]. An important process in the line of sight tracking system is the extraction and detection of line of sight characteristic parameters. Eyes as the most important visual organ contains a wealth of sight feature parameters such as the pupil center. Therefore, a fast, accurate and effective eye location algorithm is very important. Proposed Method Fig.1 shows the pupil center location algorithm flow. First, detect face region by the skin color model in the YCbCr color space. Then, using gray level features of eye region, the candidate eye region is detected by the horizontal integral gray projection algorithm [2, 3, 4]. Finally, apply the radial symmetry transform to the candidate eye region to locate pupil center. Face Detection Based on Skin Color Segmentation. Skin color is one of the most striking features of color face images, which is relatively concentrative and stable, and not affected by the brightness of the image. Skin color has a strong clustering feature. Use the ellipse skin color model proposed by Hsu[5] based on YCbCr color space, Where the Y represents the brightness information, and Cb and Cr represent the blue chrominance component the red chrominance component respectively. Ellipse skin color model is shown in Eq.1 and Eq.2, Where Cband Cr represent the original blue component and red component in the YCbCr color space respectively, and x and y represent chrominance components after transformation. Bring X, Y into the left of 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (ICCMCEE 2015) © 2015. The authors Published by Atlantis Press 1040

Unmanned aerial vehicles (UAVs) have become an important part of today’s world. Micro Aerial Vehicles (MAVs) are small-sized UAVs that are suited for closed spaces, indoor or covert applications. Due to their small size and limited energy and load they can bear, there are several challenges in intelligent control of these vehicles. Also, due to their limitations, using energy-consuming sensors and complex control strategies which need high computational power, is not an option. To overcome MAV’s limitations, researchers are working on visual navigation systems. In this paper, we present an efficient strategy for the guidance and navigation of a 30gr MAV in indoor environments using a mixture of visual and inertial sensors. By taking inspiration from nature and flying insects, we combined optical flow and stereopsis methods to detect stationary obstacles and avoid collisions. Our control system uses two lateral cameras to avoid collision with lateral walls. To detect frontal walls and obstacles, instead of using optical flow methods, we use two frontal cameras with the same viewing direction and calculate the discrepancy between their images to detect walls and obstacles. For guidance, we designed a guidance system that functions well with our obstacle detection system and only needs destination and MAV’s current coordinates. By implementing our control system on a simulated coaxial helicopter, we were able to record more than 150 hours of collision-free flight and successful guidance to appointed destinations. Our control system is best suited for small sized UAVs in indoor environments and only needs inexpensive sensors and microcontrollers to implement

This paper proposes a new algorithm for abnormal behavior detection in surveillance video. Firstly, the motion information image of each frame is constructed by calculating the optical flow size and the angle difference between the optical flow vectors between consecutive frames, and then the obtained motion image information is input into the convolutional neural network (CNN) for training, and used for video abnormal behavior detection. In the algorithm, the motion information image generated based on optical flow information can provide the motion information features in the video image more accurately, which makes it easier to distinguish the normal behavior and abnormal behavior of the video. The experiment of this algorithm is carried out on the commonly used data set PETS 2009. Experimental results show that the proposed method and other algorithms have a significant improvement in the accuracy of abnormal behavior detection.

This study adopted an improved fuzzy clustering and optical flow method for the multiscale identification and forecasting of a cloud system based on the cloud images from a 10.8-micron infrared channel of the Advanced Geosynchronous Radiation Imager. First, we used the locally constrained fuzzy c-means (FCM) clustering method to identify typhoon-dominant cloud systems. Second, we coupled the background field-constrained optical flow method with the semi-Lagrangian scheme to forecast typhoon-dominant cloud systems. The experimental results for Typhoon Maria showed that the improved FCM method was able to effectively identify changes in the cloud system while retaining its edge information through the effective removal of the offset field. The identified dominant cloud system was consistent with the precipitation field of the Global Precipitation Measurement mission. We optimized the semi-Lagrangian nonlinear extrapolation of the optical flow field by introducing background field information, thus improving the forecast accuracy of the optical flow field. Based on the assessment indicators of structural similarity, normalized mutual information, peak signal-to-noise ratio, relative standard deviation, and root mean square error, the forecast results demonstrated that the forecast effect of the background field-constrained optical flow method was better than that of the standard optical flow method.

Digital subtraction angiography is the gold standard vascular imaging and it is used for all endovascular treatment of intracranial anerysms. Optical flow imaging has been described as a potential method to evaluate cerebral hemodynamics through DSA. In this study, we aimed to compare the flow patterns measured during angiography, by using an optical flow method, with those measured by using computational fluid dynamics in intracranial aneurysms. A consecutive series of 21 patients harboring unruptured saccular intracranial aneurysms who underwent diagnostic angiography before treatment was considered. High-frame-rate digital subtraction angiography was performed to obtain an intra-aneurysmal velocity field by following the cardiac-modulated contrast wave through the vascular structures by using optical flow principles. Additionally, computational fluid dynamics modeling was performed for every case by using patient-specific inlet-boundary conditions measured with the optical flow method from both DSA and 3D rotational angiography datasets. Three independent observers compared qualitatively both the inflow direction and the apparent recirculation in regular DSA, optical flow images, and computational fluid dynamics flow patterns for each patient; κ statistics were estimated. We included 21 patients. In 14 of these 21, the flow patterns were conclusive and matching between the optical flow images and computational fluid dynamics within the same projection view (κ = .91). However, in only 8 of these 14 patients the optical flow images were conclusive and matching regular DSA images (observer κ = 0.87). In 7 of the 21 patients, the flow patterns in the optical flow images were inconclusive, possibly due to improper projection angles. The DSA-based optical flow technique was considered qualitatively consistent with computational fluid dynamics outcomes in evaluating intra-aneurysmal inflow direction and apparent recirculation. Moreover, the optical flow technique may provide the premises for new solutions for improving the visibility of flow patterns when contrast motion in DSA is not apparent. This technique is a diagnostic method to evaluate intra-aneurysmal flow patterns and could be used in the future for validation and patient evaluation.

Paper 100020I, 11 S.

This paper proposed a fast facedetection method based on the skin color feature and local binary gradient feature. First, according to the clustering of human skin color in the YCbCr color space, the skin color area is detected in the image. Then, it is coarsely fast judged whether if the face is in the skin color area. Finally, the local binary gradient feature is used to judge face accurately, and the weights of the local binary gradient feature is found by using AdaBoost train algorithm. For improved the efficiency of algorithm, the algorithm is accelerated by using the method of the integral image, the cascade classifier and the search order from big to small. The algorithm was tested by a set with 450 color images which the size is 896 ×592. It can find that the average detection time of new algorithm has reduced about 17.1% compare with the face detection algorithm of Paul Viola. The detection accuracy is similar with algorithm of Paul Viola.

Enhanced river suspended sediment concentration identification via multimodal video image, optical flow, and water temperature data fusion

The article is devoted to the study of the optical flow method for detecting changes in the shooting angle of city surveillance cameras. The study was conducted using data obtained from surveillance cameras in Kazan. The study was conducted under conditions of a static position of the cameras, as well as under conditions of their vertical and horizontal shift by 10 to 50 degrees. For each of the conditions, the corresponding data samples were used. To study the optical flow method, four algorithms for its implementation were used. Two algorithms were used for a sparse flow (Lucas-Kanade and Pyramidal Lucas-Kanade), and two more were used for a dense flow (Dense Inverse Search and FarneBack). To evaluate their effectiveness, and, consequently, the optical flow method, the following metrics were calculated: flow value, execution time in seconds, average shift value, proportion of non-zero vectors, shift consistency. Based on the obtained results, several important conclusions were made. With a static position of the camera, the average shift value for dense flows was significantly higher than for sparse flows. This increases the chance of obtaining false positive results, especially at night. With controlled camera displacement, the average shift value for the Lucas-Kanade and FarneBack algorithms was almost zero, which indicates their inability to reliably detect displacement. The Dense Inverse Search algorithm turned out to be the most sensitive to displacements, and Pyramidal Lucas-Kanade detected displacements, but with less accuracy. Dense flow algorithms are inferior in speed to sparse flow algorithms. Optical flow algorithms do not respond to text elements of the image. Thus, the study solved the problem. The study allowed us to evaluate the effectiveness of the selected method and algorithms in terms of the accuracy of calculating the average shift and resistance to dynamic objects. The optical flow method and its algorithmic implementation Pyramidal Lucas-Kanade are best suited for detecting the displacement of CCTV cameras in an urban environment. Minimization of false positives when the camera is stationary, combined with reliable detection of shifts, makes it an optimal choice, and immunity to text elements in the image increases its effectiveness.

The principle of an optical flow method is formulated for neutron radiography (NR) flow diagnostics to determine high-resolution velocity fields of two-phase flows. This method is based on solving an optical flow equation derived from the continuous equation for NR image analysis as an inverse problem using a variational method. The mathematical definition and physical meaning of the optical flow in NR images of a two-phase flow are clarified. As an example, the optical flow method is used to extract complex velocity fields from a time sequence of dynamic NR images acquired in an air–water two-phase flow in a flat bubbler.

Key frame extraction is an important component of video analysis, and has gradually become a research hotspot in the computer vision community in recent years. Early key frame extraction algorithms were mostly based on fixed time intervals, and their accuracy could not meet practical application requirements. Thanks to the rapid development of machine learning technology, key frame extraction algorithms based on image quality, motion analysis, and deep learning are gradually becoming mature. Although the above methods significantly improve the accuracy of key frame extraction, few work pay attention to the extraction effect in different scenarios. In this article, selecting the key frame extraction algorithm based on optical flow method, we compared the key frame extraction algorithm based on optical flow method in detail for different video scenarios. Extensive experimental results demonstrate the robuteness of key frame extraction algorithm based on optical flow.

Accurate inverse-consistent symmetric optical flow for 4D CT lung registration

The seminal work of Horn and Schunck is the first variational method for optical flow estimation. It introduced a novel framework where the optical flow is computed as the solution of a minimization problem. From the assumption that pixel intensities do not change over time, the optical flow constraint equation is derived. This equation relates the optical flow with the derivatives of the image. There are infinitely many vector fields that satisfy the optical flow constraint, thus the problem is ill-posed. To overcome this problem, Horn and Schunck introduced an additional regularity condition that restricts the possible solutions. Their method minimizes both the optical flow constraint and the magnitude of the variations of the flow field, producing smooth vector fields. One of the limitations of this method is that, typically, it can only estimate small motions. In the presence of large displacements, this method fails when the gradient of the image is not smooth enough. In this work, we describe an implementation of the original Horn and Schunck method and also introduce a multi-scale strategy in order to deal with larger displacements. For this multi-scale strategy, we create a pyramidal structure of downsampled images and change the optical flow constraint equation with a nonlinear formulation. In order to tackle this nonlinear formula, we linearize it and solve the method iteratively in each scale. In this sense, there are two common approaches: one approach that computes the motion increment in the iterations; or the one we follow, that computes the full flow during the iterations. The solutions are incrementally refined over the scales. This pyramidal structure is a standard tool in many optical flow methods.