Scale-adaptive Method Research Articles

Autonomous landing algorithm of UAV based on Target tracking

The target tracking technology in the UAV autonomous landing process is studied. Based on the analysis of the occlusion, background change, and variable scale problems in the target tracking process, a scale adaptive method for UAV target tracking based on KCF (Kernelized Correlation Filter) algorithm is proposed. The original KCF target tracking framework is adopted to achieve the target position prediction function. Secondly, to address the variable scale problem of moving targets and the target loss problem caused by scale changes, the scale filter of the DSST target tracking algorithm is used. The algorithm is for estimating the scale of moving targets and generating a small-scale filter estimate to improve the tracking efficiency of the UAV for moving targets and reduce the target loss rate. The final test on the OTB dataset showed a 4% improvement in variable scale accuracy and a 2.9% improvement in variable scale success rate. A Gazebo 3D environment scene was set up under the Linux system, and UAV autonomous landing simulation experiments were conducted to verify the improved algorithm’s real-time effectiveness in the UAV autonomous landing application. The simulation results show that the improved KCF target tracking algorithm has real-time performance in the UAV autonomous landing trajectory tracking process.

Scale Adaptive Small Objects Detection Method in Complex Agricultural Environment: Taking Bees as Research Object

Scale Adaptive Small Objects Detection Method in Complex Agricultural Environment: Taking Bees as Research Object

Design of Intelligent Early Warning Robot System for Warehouse Autonomous Patrol based on Scale Estimation Algorithm of Probability Theory

In view of the backward monitoring status in the traditional safety management of hazardous chemicals warehouse, a patrol robot for hazardous chemicals warehouse with autonomous patrol stereo detection, intelligent early warning and remote monitoring functions is designed, and the design process of related functions of autonomous patrol navigation subsystem, environmental detection subsystem and remote monitoring subsystem is discussed from two aspects of hardware and software. Because both the camera and the target may be in motion, it is necessary to consider the scale change of target influence caused by relative distance. In order to overcome the shortcoming of invariable scale in Mean Shift algorithm, this paper adopts a scale adaptive method to estimate the size of the target, adjusts the parameters of kernel function by using ellipse feature, and takes the parameters of the current frame as the initial data of the next frame, so as to achieve the effect of adaptively adjusting the target scale. The test results show that the system can effectively traverse the map of hazardous chemicals warehouse, accurately assess the leakage of hazardous chemicals and the environmental safety of warehouse, and give early warning. The remote monitoring platform greatly facilitates the remote management of the warehouse.

A Novel Vision‐Based PRPL Multistage Image Processing Algorithm for Autonomous Aerial Refueling

Autonomous aerial refueling (AAR) technology can increase the flight endurance of unmanned air vehicles (UAVs) effectively. Drogue detection and target tracking method are significant for probe‐drogue refueling system in the docking stage. This paper proposes a novel vision‐based multistage image processing algorithm of drogue detection and target tracking for AAR. This algorithm divides the whole task into four stages: preprocessor, recognizer, predictor, and locker (PRPL). The adaptive threshold segmentation (ATS) algorithm and support vector machine (SVM) classifier are utilized in preprocessor and recognizer for drogue detection. An improved kernelized correlation filter (IKCF) tracking algorithm and scale adaptive method by window position as well as image resolution adjusted are adopted in predictor and locker for target tracking in complex dynamic environments. Finally, the proposed PRPL multistage image processing strategy is tested using an autonomous aerial refueling testbed. The results indicate that the proposed algorithm achieves high precision, good reliability, and real‐time capability compared with conventional algorithms. The average processing time is within 11 ms in various environments, which can meet the requirement for drogue detection and tracking in AAR.

高置信度的尺度自适应核相关跟踪方法

高置信度的尺度自适应核相关跟踪方法

Robust Estimation of Arrival Time of Complex Noisy Partial Discharge Pulse in Power Cables Based on Adaptive Variational Mode Decomposition

Periodic narrowband signals and white noise are the main interferences in online detection and localization of cable partial discharge (PD), however, existing research has always focused on the white noise suppression only, which is not in line with the actual scene. A novel de-noising method for effectively extracting random PD pulse from complex and strong interferences is proposed in this paper and applied to PD localization. Firstly, an improved adaptive variational mode decomposition (AVMD) is used to decompose periodic narrowband interference, white noise, and PD signal into different intrinsic mode. According to the characteristic that the power of intrinsic mode component of periodic narrowband interference in the discrete Fourier transformation (DFT) power spectrum is much larger than that of PD and white noise, the periodic narrowband is removed out. In order to effectively filter out white noise, a scale adaptive wavelet packet decomposition method based on correlation coefficient is proposed, which decomposes the signal into high, middle, and low-frequency components. The components with low frequency, small amplitude are removed out as the white noise interference according to the threshold method, and the residual is the de-noising PD signal. Experimental results show that the proposed method can robustly suppress the interference of periodic narrowband signal and white noise, and effectively preserve the essential characteristics of the real PD signal. In the multi-sensor travelling wave based localization system of cable PD source using time-varying kurtosis, accurate estimation of first arrival time of PD pulse can be achieved by the de-noising results.

Scale-adaptive estimation of mixed geographically weighted regression models

Mixed geographically weighted regression (GWR) models, a combination of linear and spatially varying coefficient models, have found their applications in a variety of disciplines including economic modelling for geo-referenced data analysis. Generally, different explanatory variables may operate at different spatial scales, leading to different levels of spatial heterogeneity of the varying coefficients. To deal with such a multiscale problem, we propose a scale-adaptive method to calibrate mixed GWR models, in which a different bandwidth is separately assumed for each spatially varying coefficient and is selected based on the backfitting procedure. Extensive simulations with different spatial layouts and a real-world example based on the Dublin voter turnout data demonstrate that the scale-adaptive method can not only significantly improve the estimation accuracy of the spatially varying coefficients, but also provide valuable information on the scale at which each explanatory variable operates.

Fast Scale Estimation Method in Object Tracking

Accurate and fast scale estimation of targets is a challenging research problem in visual object tracking. Most trackers employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and it struggles when encountered with large scale variations. In this paper, a scale adaptive method is proposed, which not only improves the tracking performance, but also greatly reduces the computational costs and improves the tracking speed. Based on the scale estimation method of SAMF, the original 7 fixed scale sizes were reduced to 3, and an adaptive scale size was added. Three fixed scales were used to determine the direction of scale change, and the APCE change rate of the current frame and the previous frame was used to control another adaptive scale size. Finally, the optimal scale estimation was determined. Additionally, we investigate the update strategy to further improve the tracking accuracy. Extensive experiments on OTB50, OTB100 and VOT-ST2019 datasets demonstrate that the proposed method can tackle challenging videos well compared with baseline tracker. On OTB, we obtain a gain of 7.0% in Distance Precision, and 18.8% in Centre Location Error on the selected 43 videos with scale variation attribute, and a mean gain of 6.2% in Precision and 4.6% in Success plots on OTB50, compared with the baseline tracker SAMF. Furthermore, the proposed approach improves the tracking speed by 34% in FPS compared with SAMF.

A scale-adaptive method for urban rainwater harvesting simulation.

At a building or dwelling scale, accurate evaluation of the water savings potential from rainwater harvesting (RWH) can be achieved by simulating the performance of the RWH system using a balance equations model. At an urban scale, water savings potential is usually estimated from the balance between the annual rainfall and annual water consumption. This approach has limited accuracy since it assumes an infinite storage capacity and it disregards the variability of the ratio between the water collected and water consumed in each building. This paper presents a methodology to evaluate rainwater harvesting potential at an urban level taking into consideration buildings' characteristics and consumption pattern. The complexity of the model is balanced with the format and detail of the information available to allow fast and easy implementation with few resources. The proposed methodology is applied to the city of Lisbon, Portugal, located on the Atlantic coast of the Mediterranean climate region. The results demonstrate water savings potential ranging from 16 to 86% depending on the buildings and occupancy characteristics. The spatial variability of the rainfall in the city of Lisbon was found to be negligible for rainwater harvesting potential evaluation.

Scale-Adaptive Tracking Method by Combining Kernelized Correlation Filter with Geometric Estimation

Scale-Adaptive Tracking Method by Combining Kernelized Correlation Filter with Geometric Estimation

A Novel Enhanced Gray Scale Adaptive Method for Prediction of Breast Cancer.

Breast cancer is the important problem across the globe in which, most of the women are suffering without knowing the causes and effects of the cancer cells. Mammographic is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent techniques are suggested for the detection of Microcalcification, Clusters, Masses, Spiculate lesions, Asymmetry and Architectural distortions in the mammograms. But the prediction of the cancer levels needs more research light. For the determination of the higher level of accuracy and prediction, the proposed algorithm called Enhanced Gray Scale Adaptive Method (EGAM) which works on the principle of combination of K-GLCM and Extreme Fuzzy Learning Machines (EFLM). The proposed algorithm has achieved 99% accuracy and less computation time in terms of classification, detection and prediction when compared with the existing intelligent algorithms.

Rigid and non-rigid registration of polarized light imaging data for 3D reconstruction of the temporal lobe of the human brain at micrometer resolution

To understand the spatial organization as well as long- and short-range connections of the human brain at microscopic resolution, 3D reconstruction of histological sections is important. We approach this challenge by reconstructing series of unstained histological sections of multi-scale (1.3μm and 64μm) and multi-modal 3D polarized light imaging (3D-PLI) data. Since spatial coherence is lost during the sectioning procedure, image registration is the major step in 3D reconstruction. We propose a non-rigid registration method which comprises of a novel multi-modal similarity metric and an improved regularization scheme to cope with deformations inevitably introduced during the sectioning procedure, as well as a rigid registration approach using a robust similarity metric for improved initial alignment. We also introduce a multi-scale feature-based localization and registration approach for mapping of 1.3μm sections to 64μm sections and a scale-adaptive method that can handle challenging sections with large semi-global deformations due to tissue splits. We have applied our registration method to 126 consecutive sections of the temporal lobe of the human brain with 64μm and 1.3μm resolution. Each step of the registration method was quantitatively evaluated using 10 different sections and manually determined ground truth, and a quantitative comparison with previous methods was performed. Visual assessment of the reconstructed volumes and comparison with reference volumes confirmed the high quality of the registration result.

Online Scale Adaptive Visual Tracking Based on Multilayer Convolutional Features.

Convolutional neural networks can efficiently exploit sophisticated hierarchical features which have different properties for visual tracking problem. In this paper, by using multilayer convolutional features jointly and constructing a scale pyramid, we propose an online scale adaptive tracking method. We construct two separate correlation filters for translation and scale estimations. The translation filters improve the accuracy of target localization by a weighted fusion of multiple convolutional layers. Meanwhile, the separate scale filters achieve the optimal and fast scale estimation by a scale pyramid. This design decreases the mutual errors of translation and scale estimations, and reduces computational complexity efficiently. Moreover, in order to solve the problem of tracking drifts due to the severe occlusion or serious appearance changes of the target, we present a new adaptive and selective update mechanism to update the translation filters effectively. Extensive experimental results show that our proposed method achieves the excellent overall performance compared with the state-of-the-art methods.

Addressing location uncertainties in GPS-based activity monitoring: A methodological framework.

Location uncertainty has been a major barrier in information mining from location data. Although the development of electronic and telecommunication equipment has led to an increased amount and refined resolution of data about individuals' spatio-temporal trajectories, the potential of such data, especially in the context of environmental health studies, has not been fully realized due to the lack of methodology that addresses location uncertainties. This article describes a methodological framework for deriving information about people's continuous activities from individual-collected Global Positioning System (GPS) data, which is vital for a variety of environmental health studies. This framework is composed of two major methods that address critical issues at different stages of GPS data processing: (1) a fuzzy classification method for distinguishing activity patterns; and (2) a scale-adaptive method for refining activity locations and outdoor/indoor environments. Evaluation of this framework based on smartphone-collected GPS data indicates that it is robust to location errors and is able to generate useful information about individuals' life trajectories.

A Scale Adaptive Method Based On Quaternion Correlation in Object Tracking

In this paper, we proposed a scale adaptive Kalman filter algorithm based on quaternion correlation of color image. First, Kalman filter is used to estimate the object motion direction. The correlation of object and the searching window image is calculated to get the accurate position of the object. The scale adaptive method is efficient to the situation of object size changing. In order to reduce the influence of illumination, we proposed to use HSV color space instead of RGB color space. Experiments results showed that the algorithm can detect the object correctly even the size and the color of the object changed.

Multi-Scale Segmentation of Forest Areas and Tree Detection in LiDAR Images by the Attentive Vision Method

A scale-adaptive method for object detection and LiDAR image segmentation in forest areas using the attentive vision approach to remote sensing image analysis is proposed. It provides an effective solution to the general task of object segmentation defined as the subdivision of image plan into multiple objects regions against the background region. This method represents a multi-scale analysis of LiDAR images by an attention operator at different scale ranges and for all pixel locations to detect feature points. Besides the initial height image, the operator also uses primitive feature maps (components) to reliably detect objects of interest such as individual trees or entire forest stands. As a result, feature points representing the optimal seed locations for region-growing segmentation are extracted and scale-adaptive region growing is applied at the seed locations. At the second level, the final segmentation by the scale-adaptive region growing provides delineation of individual tree crowns. The conducted experiments confirmed the reliability of the proposed method and showed its high potential in LiDAR image analysis for object detection and segmentation.

중심 이동 기반의 스케일 적응적 물체 추적 알고리즘

본 논문에서는 물체를 구성하고 있는 컬러들의 기하학적 중심을 이용하여 물체의 크기에 적응적인 추적 알고리즘을 제안한다. 대부분의 크기 적응적 알고리즘은 표적윈도우의 크기를 결정하기 위하여 히스토그램을 사용한다. 그러나, 이러한 방법은 표적의 배경에 표적의 색상과 유사한 물체가 존재하거나 표적의 일부분이 폐색되었을 때 표적의 크기를 추정하는데 실패한다. 이것은 히스토그램이 영역에 대한 기하학적인 공간정보를 상실한채 표적 컬러의 화소수하고만 연관되기 때문이다. 이러한 분석을 바탕으로 본 논문은 표적 컬러의 화소수의 변화에 상대적으로 덜 민감한 표적의 컬러 중심을 이용한 크기 적응 알고리즘을 제안한다. 컬러의 중심들은 공간정보를 가지고 있기 때문에 컬러중심과 표적 영역의 크기에는 직접적인 상관관계가 존재한다. 표적의 크기 변화를 추정하기 위하여 각각의 표적 컬러에 대한 줌팩터를 추정한 후, 적절한 필터링 과정을 통해 하나의 줌팩터를 추정한다. 제안한 크기 추정 알고리즘은 중심이동 기반의 추적 알고리즘과 결합된다. 제안된 크기 적응적 추적 알고리즘은 배경에 유사한 컬러가 존재하는 경우에도 안정적으로 작동하는 것을 실험으로 검증한다. In this paper, we propose a stable scale adaptive tracking method that uses centroids of the target colors. Most scale adaptive tracking methods have utilized histograms to determine target window sizes. However, in certain cases, histograms fail to provide good estimates of target sizes, for example, in the case of occlusion or the appearance of colors in the background that are similar to the target colors. This is due to the fact that histograms are related to the numbers of pixels that correspond to the target colors. Therefore, we propose the use of centroids that correspond to the target colors in the scale adaptation algorithm, since centroids are less sensitive to changes in the number of pixels that correspond to the target colors. Due to the spatial information inherent in centroids, a direct relationship can be established between centroids and the scale of target regions. Generally, after the zooming factors that correspond to all the target colors are calculated, the unreliable zooming factors are filtered out to produce a reliable zooming factor that determines the new scale of the target. Combined with the centroid based tracking algorithm, the proposed scale adaptation method results in a stable scale adaptive tracking algorithm. It tracks objects in a stable way, even when the background colors are similar to the colors of the object.