Aerial Image Sequences Research Articles

Target detection and classification via EfficientDet and CNN over unmanned aerial vehicles.

Advanced traffic monitoring systems face significant challenges in vehicle detection and classification. Conventional methods often require substantial computational resources and struggle to adapt to diverse data collection methods. This research introduces an innovative technique for classifying and recognizing vehicles in aerial image sequences. The proposed model encompasses several phases, starting with image enhancement through noise reduction and Contrast Limited Adaptive Histogram Equalization (CLAHE). Following this, contour-based segmentation and Fuzzy C-means segmentation (FCM) are applied to identify foreground objects. Vehicle detection and identification are performed using EfficientDet. For feature extraction, Accelerated KAZE (AKAZE), Oriented FAST and Rotated BRIEF (ORB), and Scale Invariant Feature Transform (SIFT) are utilized. Object classification is achieved through a Convolutional Neural Network (CNN) and ResNet Residual Network. The proposed method demonstrates improved performance over previous approaches. Experiments on datasets including Vehicle Aerial Imagery from a Drone (VAID) and Unmanned Aerial Vehicle Intruder Dataset (UAVID) reveal that the model achieves an accuracy of 96.6% on UAVID and 97% on VAID. The results indicate that the proposed model significantly enhances vehicle detection and classification in aerial images, surpassing existing methods and offering notable improvements for traffic monitoring systems.

Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences

Vehicle detection and classification are the most significant and challenging activities of an intelligent traffic monitoring system. Traditional methods are highly computationally expensive and also impose restrictions when the mode of data collection changes. This research proposes a new approach for vehicle detection and classification over aerial image sequences. The proposed model consists of five stages. All of the images are preprocessed in the first stage to reduce noise and raise the brightness level. The foreground items are then extracted from these images using segmentation. The segmented images are then passed onto the YOLOv8 algorithm to detect and locate vehicles in each image. The feature extraction phase is then applied to the detected vehicles. The extracted feature involves Scale Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB), and KAZE features. For classification, we used the Deep Belief Network (DBN) classifier. Based on classification, the experimental results across the three datasets produced better outcomes; the proposed model attained an accuracy of 95.6% over Vehicle Detection in Aerial Imagery (VEDAI) and 94.6% over Vehicle Aerial Imagery from a Drone (VAID) dataset, respectively. To compare our model with the other standard techniques, we have also drawn a comparative analysis with the latest techniques in the research.

Vision-based aerial image mosaicking algorithm with object detection

Aerial image sequence mosaicking is one of the challenging research fields in computer vision. To obtain large-scale orthophoto maps with object detection information, we propose a vision-based image mosaicking algorithm without any extra location data. According to object detection results, we define a complexity factor to describe the importance of each input Image and dynamically optimize the feature extraction process. The feature points extraction and matching processes are mainly guided by the speeded-up robust features (SURF) and the grid motion statistic (GMS) algorithm respectively. A robust reference frame selection method is proposed to eliminate the transformation distortion by searching for the center area based on overlaps. Besides, the sparse Levenberg-Marquardt (LM) algorithm and the heavy occluded frames removal method are Applied to reduce accumulated errors and further improve the mosaicking performance. The proposed algorithm is performed by using multithreading and graphics processing unit (GPU) acceleration on several aerial image datasets. Extensive experiment results demonstrate that our algorithm outperforms most of the existing aerial image mosaicking methods in visual quality while guaranteeing a high calculation speed.

Improving the Model for Person Detection in Aerial Image Sequences Using the Displacement Vector: A Search and Rescue Scenario

Recent results in person detection using deep learning methods applied to aerial images gathered by Unmanned Aerial Vehicles (UAVs) have demonstrated the applicability of this approach in scenarios such as Search and Rescue (SAR) operations. In this paper, the continuation of our previous research is presented. The main goal is to further improve detection results, especially in terms of reducing the number of false positive detections and consequently increasing the precision value. We present a new approach that, as input to the multimodel neural network architecture, uses sequences of consecutive images instead of only one static image. Since successive images overlap, the same object of interest needs to be detected in more than one image. The correlation between successive images was calculated, and detected regions in one image were translated to other images based on the displacement vector. The assumption is that an object detected in more than one image has a higher probability of being a true positive detection because it is unlikely that the detection model will find the same false positive detections in multiple images. Based on this information, three different algorithms for rejecting detections and adding detections from one image to other images in the sequence are proposed. All of them achieved precision value about 80% which is increased by almost 20% compared to the current state-of-the-art methods.

Online Ground Multitarget Geolocation Based on 3-D Map Construction Using a UAV Platform

Geolocating multiple targets of interest on the ground from an aerial platform is an important activity in many applications, such as visual surveillance. However, due to the limited measurement accuracy of commonly used airborne sensors (including altimeters, accelerometer, gyroscopes, and so on) and the small size, complex motion, and a large number of ground targets in aerial images, most of the current unmanned aerial vehicle (UAV)-based ground target geolocation algorithms have difficulty in obtaining accurate geographic location coordinates online, especially at middle and high altitudes. To solve these problems, in this article, a novel online ground multitarget geolocation framework using a UAV platform is proposed, which minimizes the introduction of sensor error sources and uses only monocular aerial image sequences and global positioning system (GPS) data to perform parallel processing of target detection and rapid 3-D sparse geographic map construction and target geographic location estimations, thereby improving the accuracy and speed of ground multitarget online geolocation. In this framework, a detection algorithm based on deep learning is first adopted to improve the accuracy and robustness of small target detection in aerial images by constructing an aerial image dataset. Then, we propose a novel target geolocation algorithm based on 3-D map construction, which combines continuous images and GPS data collected online using a UAV platform to generate a 3-D geographic map and accurately estimates the GPS location of the target center pixel through projection and triangulation of the map points on the image. Finally, we design a data transmission architecture that selects multiple processes to perform image acquisition, target detection, and target geolocation tasks in parallel and utilizes database communication between the processes to achieve accurate online geolocation of ground targets in aerial images, regardless of whether the target is in a static or moving state. To evaluate the effectiveness of the proposed framework, we build an online ground multitarget geolocation system using a quad-rotor UAV and carry out a large number of experiments in simulations and real environments. Qualitative and quantitative experimental results proved that the framework can accurately locate ground targets in various complex environments, such as parks, highways, schools, cities, different flight altitudes (50–2000 m), and different attitude angles, and the average positioning error is approximately 1 m at 2000 m for cities with rich 3-D structures.

A Deep Learning Architecture for 3D Mapping Urban Landscapes

In this paper, an approach through a Deep Learning architecture for the three-dimensional reconstruction of outdoor environments in challenging terrain conditions is presented. The architecture proposed is configured as an Autoencoder. However, instead of the typical convolutional layers, some differences are proposed. The Encoder stage is set as a residual net with four residual blocks, which have been provided with the necessary knowledge to extract the feature maps from aerial images of outdoor environments. On the other hand, the Decoder stage is set as a Generative Adversarial Network (GAN) and called a GAN-Decoder. The proposed network architecture uses a sequence of the 2D aerial image as input. The Encoder stage works for the extraction of the vector of features that describe the input image, while the GAN-Decoder generates a point cloud based on the information obtained in the previous stage. By supplying a sequence of frames that a percentage of overlap between them, it is possible to determine the spatial location of each generated point. The experiments show that with this proposal it is possible to perform a 3D representation of an area flown over by a drone using the point cloud generated with a deep architecture that has a sequence of aerial 2D images as input. In comparison with other works, our proposed system is capable of performing three-dimensional reconstructions in challenging urban landscapes. Compared with the results obtained using commercial software, our proposal was able to generate reconstructions in less processing time, with less overlapping percentage between 2D images and is invariant to the type of flight path.

Aerial Infrared Object Tracking via an improved Long-term Correlation Filter with optical flow estimation and SURF matching

At present, there are many excellent algorithms in the field of visual object tracking. The correlation filter algorithms are more suitable for infrared object tracking, for the tracking performance is the best. Especially, the long-term correlation tracking has received much attention, owing to its ability to handle the problems of universal tracking (e.g., slight deformation, small-displacement motion, partial occlusion and out of view). However, there are three imperfections such that it fails to solve the problem of rapid motion, it cannot cope with the problem of boundary effect perfectly, and it has poor tracking effect in the case of severe occlusion and severe deformation. Aiming at the problem of rapid motion, the FlowNet 2.0 is introduced to accomplish optical flow estimation, offering motion information and predicting trajectory change process. Aiming to address the concern of boundary effect perfectly, the adjustable Gaussian window is effective to separate the object and the background, improving classifier discrimination. Aiming at the issue of poor tracking effect in the case of severe occlusion and severe deformation, the SURF feature-based matching method is effective to accurately track object and improve the infrared object tracking performance. In addition, the ratio between average peak-to-correlation energy (APCE) and its historical average, as a further complement of maximum response, is introduced to achieve the online updating mechanism, jointly determining whether the tracker needs to be updated, the SURF matching needs to be carried out or the tracker needs to be initialized by re-detector. Our algorithm is validated on synthetic infrared aerial object image sequences, real infrared thermal aerial object image sequences and a public database named AMCOM FLIR respectively. The extensive experimental testify that our improved approach achieves an optimal effect for aerial infrared object tracking in terms of precision plot, success plot and speed.

MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms

We present MultEYE, a traffic monitoring system that can detect, track, and estimate the velocity of vehicles in a sequence of aerial images. The presented solution has been optimized to execute these tasks in real-time on an embedded computer installed on an Unmanned Aerial Vehicle (UAV). In order to overcome the limitation of existing object detection architectures related to accuracy and computational overhead, a multi-task learning methodology was employed by adding a segmentation head to an object detector backbone resulting in the MultEYE object detection architecture. On a custom dataset, it achieved 4.8% higher mean Average Precision (mAP) score, while being 91.4% faster than the state-of-the-art model and while being able to generalize to different real-world traffic scenes. Dedicated object tracking and speed estimation algorithms have been then optimized to track reliably objects from an UAV with limited computational effort. Different strategies to combine object detection, tracking, and speed estimation are discussed, too. From our experiments, the optimized detector runs at an average frame-rate of up to 29 frames per second (FPS) on frame resolution 512 × 320 on a Nvidia Xavier NX board, while the optimally combined detector, tracker and speed estimator pipeline achieves speeds of up to 33 FPS on an image of resolution 3072 × 1728. To our knowledge, the MultEYE system is one of the first traffic monitoring systems that was specifically designed and optimized for an UAV platform under real-world constraints.

Local Feature Performance Evaluation for Structure-From-Motion and Multi-View Stereo Using Simulated City-Scale Aerial Imagery

Ubiquitous low cost multi-rotor and fixed wing drones or unmanned aerial vehicles (UAVs) have accelerated the need for reliable, robust, and scalable Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipelines suitable for a variety of flightpath trajectories especially in degraded environments. Feature tracking being a core part of SfM and MVS, is essential for multiview scene modeling and perception, but difficult to evaluate in large scale datasets due to the lack of sufficient ground-truth. For large-scale aerial imagery, accurate camera orientation and dense 3D point cloud accuracy can be used to assess the impact of accurate feature localization and track length. We propose a novel view simulation (or synthesis) framework which generates visually realistic new unseen camera views for feature detection using known high fidelity camera poses for modeling. Seven state-of-the-art local handcrafted and learning-based features are quantitatively evaluated for robustness and matchability within the SfM and MVS pipelines using the open source COLMAP software. Our experimental results provide performance rankings of each feature, using twelve different evaluation metrics across three synthetic city-wide aerial image sequences. We show that recent learned features, SuperPoint and LF-Net, have not only reached the quality of the best handcrafted features like SIFT and SURF, but now outperform them in terms of more accurate 3D camera pose estimates and longer feature tracks. SuperPoint produces 1.51 meter average position error and 0.03° average angular error, while SIFT remains competitive (second best for pose and overall) with 1.78 meter and 0.11° errors respectively.

Seven decades of hydrogeomorphological changes in a near‐natural (Sense River) and a hydropower‐regulated (Sarine River) pre‐Alpine river floodplain in Western Switzerland

ABSTRACTHydropower alteration of the natural flow and sediment regime can severely degrade hydromorphology, thereby threatening biodiversity and overall ecosystem processes of rivers and their floodplains. Using sequences of aerial images, we quantified seven decades (1938/1942–2013) of spatiotemporal changes in channel and floodplain morphology, as well as changes in the physical habitats, of three floodplain river reaches of the Swiss pre‐Alps, two hydropower‐regulated and one near‐natural. In the Sarine River floodplain, within the first decades of hydropower impairment, the magnitude and frequency of flood events (Q2, Q10, Q30) decreased substantially. As a result, the area of pioneer floodplain habitats that depend on flood activity and sediment dynamic, such as bare sediments, decreased dramatically by approximately 95%. However, by 2013 vegetated areas had generally increased in comparison to the pre‐regulation period in 1943, indicating general vegetative colonization. Between 1943 and 2013, the active channel underwent essential narrowing (up to 62% width reduction in the residual flow reach) and habitat turnover rates were very low (5% of the total floodplain area changed habitat type five to six times). In contrast, from the 1950s onwards, the near‐natural floodplain of the Sense River experienced recurrent narrowing and widening, and frequent changes between bare and vegetated areas, reflecting the shifting habitat mosaic concept typical for natural floodplains. In the three reaches investigated, we found that the active floodplain width and erosion of vegetated areas were primarily controlled by medium to large floods (Q10, Q30), which combined with reduced time intervals between ordinary floods ≥ Q2 most likely mobilized streambed sediments and limited the ability of vegetation to establish itself on bare gravel bars within the parafluvial zone. These findings can contribute to restoration action plans such as controlled flooding and sediment replenishments in the Sarine and other floodplain rivers of the Alps. © 2020 John Wiley & Sons, Ltd.

Use of geoinformation systems for agricultural problems

The paper considers the development of a conceptual model of a geographic informaton system for agriculture. The conceptual model of GISA, which consists of 3 modules, is ofered. The frst module provides data collecton, partcularly the sequence of aerial and space images of the site, soil data of the region, weather forecast, etc. The second module provides NDVI calculaton, determines land quality indicators, and performs forecastng indicators for subsequent periods. The third module is designed to present informaton in a user-friendly form. In partcular, it should build NDVI maps, soil maps, build tme series schedules.

Extending the conceptual model of river island development to incorporate different tree species and environmental conditions

AbstractRiparian vegetation survival and establishment in gravel‐bed rivers depends on the balance between vegetation growth and flood disturbance. We present four examples of vegetation and landform development in gaps (linear open spaces) between established islands and/or floodplain within a reach of the middle Tagliamento River, Italy. Gaps offer shelter to vegetation, supporting higher colonization success and different vegetation‐landform evolution pathways. Time sequences of aerial images track vegetation development over 30 years in the four gaps. In combination with the flood disturbance time series, we interpret vegetation dynamics and identify the fate of sexual and asexual reproduction strategies by observing vegetation expansion from lines of young plants and shrubs and from uprooted deposited trees and pioneer islands, respectively. Analysis of image sequences reveals common features across the four gaps that are generalized to extend a conceptual model of island development. Growing conditions, disturbance energy and time (window of opportunity) between major floods are the main controls on vegetation colonization. These vary among rivers, among reaches along the same river and locally, as in the investigated gaps, allowing different tree species with different life history traits (e.g., Populus nigra, Alnus incana) to engineer local river landforms in different and complementary ways. Although the conceptual model is inspired by observations on the Tagliamento River, consideration of species life history traits and the joint influences of growing conditions, disturbance energy and windows of opportunity provide a framework that may be applied to other temperate rivers where trees drive landform development.

Geolocalization with aerial image sequence for UAVs

The estimation of geolocation for aerial images is significant for tasks like map creating, or automatic navigation for unmanned aerial vehicles (UAVs). We propose a novel geolocalization method for the UAVs using only aerial images and reference road map. The corresponding road maps of the aerial images are firstly merged into a whole mosaic image using our newly-designed aerial image mosaicking algorithm, where the relative homography transformations between road images are firstly estimated using keypoints tracking in RGB aerial images, and then further refined with registration between detected roads. The geolocalization of the aerial mosaic image is then taken as the problem of registering observed roads in the aerial images to the reference road map under the homography transformation. The registration problem is solved with our fast search algorithm based on a novel projective-invariant feature, which consists of two road intersections augmented with their tangents. Experiments demonstrate that the proposed method can localize the aerial image sequence over an area larger than 1000 km $$^2$$ within a few seconds.

Anthropic interference in mangrove areas of the Mundaú-Manguaba estuarine lagoon complex (CELMM), Alagoas (Brazil) as a case study

ABSTRACT: The mangrove is a coastal ecosystem that is present in different parts of the world. It provides various ecosystem services from food supply to the influence of climate change. Due to the development of society, this ecosystem has been subjected to significant impacts from anthropogenic activities. Therefore, the objective of this research was to evaluate the environmental impacts caused in mangrove areas that have undergone modifications as a result of anthropic activities (agricultural cultivation, deforestation, civil construction) compared with those of conserved mangrove areas. This research took place through the analysis of the temporal sequence of aerial images (Google Earth) and soil quality analysis through field collections to evaluate the chemical and biological indicators in the different land use systems. As these are permanent changes that affect the type of soil and its coverage, significant differences were obtained between the chemical and biological characteristics of the four environments, with different usage systems. The mangrove has been negatively impacted by inadequate management and land occupation. Continuity of anthropic intervention in the mangrove will promote the disappearance of this ecosystem in the long term. Among the chemical and biological attributes used for the analyses that were performed, aluminum and edaphic organisms were the ones that allowed the greatest contribution of distinction from the degree of disturbance in areas of agricultural cultivation, deforestation and civil construction/mangrove transition.

MULTIPLE VEHICLES AND PEOPLE TRACKING IN AERIAL IMAGERY USING STACK OF MICRO SINGLE-OBJECT-TRACKING CNNS

Abstract. Geo-referenced real-time vehicle and person tracking in aerial imagery has a variety of applications such as traffic and large-scale event monitoring, disaster management, and also for input into predictive traffic and crowd models. However, object tracking in aerial imagery is still an unsolved challenging problem due to the tiny size of the objects as well as different scales and the limited temporal resolution of geo-referenced datasets. In this work, we propose a new approach based on Convolutional Neural Networks (CNNs) to track multiple vehicles and people in aerial image sequences. As the large number of objects in aerial images can exponentially increase the processing demands in multiple object tracking scenarios, the proposed approach utilizes the stack of micro CNNs, where each micro CNN is responsible for a single-object tracking task. We call our approach Stack of Micro-Single- Object-Tracking CNNs (SMSOT-CNN). More precisely, using a two-stream CNN, we extract a set of features from two consecutive frames for each object, with the given location of the object in the previous frame. Then, we assign each MSOT-CNN the extracted features of each object to predict the object location in the current frame. We train and validate the proposed approach on the vehicle and person sets of the KIT AIS dataset of object tracking in aerial image sequences. Results indicate the accurate and time-efficient tracking of multiple vehicles and people by the proposed approach.

RULE-BASED MAPPING OF PARKED VEHICLES USING AERIAL IMAGE SEQUENCES

Abstract. Mapping of parking spaces in cities is a prerequisite for future applications in parking space management like community-based parking. Although terrestrial or vehicle based sensors will be the favorite data source for parking space mapping, airborne monitoring can play a role in building up city wide basis maps which include also parking spaces on ancillary and suburban roads. We present a novel framework for automatic city wide classification of vehicles in moving, stopped and parked using aerial image sequences and information from a road database. The time span of observation of a specific vehicle during an image sequence is usually not long enough to decide unambiguously, whether a vehicle stopped e.g. before a traffic light or is parking along the road. Thus, the workflow includes a vehicle detection and tracking method as well as a rule-based fuzzy-logic workflow for the classification of vehicles. The workflow classifies stopped and parked vehicles by including the neighbourhood of each vehicle via a Delaunay-Graph. The presented method reaches correctness values of around 86.3%, which is demonstrated using three different aerial image sequences. The results depend on several factors like detection quality and road database accuracy.

EFFICIENT SURFACE-AWARE SEMI-GLOBAL MATCHING WITH MULTI-VIEW PLANE-SWEEP SAMPLING

Abstract. Online augmentation of an oblique aerial image sequence with structural information is an essential aspect in the process of 3D scene interpretation and analysis. One key aspect in this is the efficient dense image matching and depth estimation. Here, the Semi-Global Matching (SGM) approach has proven to be one of the most widely used algorithms for efficient depth estimation, providing a good trade-off between accuracy and computational complexity. However, SGM only models a first-order smoothness assumption, thus favoring fronto-parallel surfaces. In this work, we present a hierarchical algorithm that allows for efficient depth and normal map estimation together with confidence measures for each estimate. Our algorithm relies on a plane-sweep multi-image matching followed by an extended SGM optimization that allows to incorporate local surface orientations, thus achieving more consistent and accurate estimates in areasmade up of slanted surfaces, inherent to oblique aerial imagery. We evaluate numerous configurations of our algorithm on two different datasets using an absolute and relative accuracy measure. In our evaluation, we show that the results of our approach are comparable to the ones achieved by refined Structure-from-Motion (SfM) pipelines, such as COLMAP, which are designed for offline processing. In contrast, however, our approach only considers a confined image bundle of an input sequence, thus allowing to perform an online and incremental computation at 1Hz–2Hz.

DEEP CROSS-DOMAIN BUILDING EXTRACTION FOR SELECTIVE DEPTH ESTIMATION FROM OBLIQUE AERIAL IMAGERY

Abstract. With the technological advancements of aerial imagery and accurate 3d reconstruction of urban environments, more and more attention has been paid to the automated analyses of urban areas. In our work, we examine two important aspects that allow online analysis of building structures in city models given oblique aerial image sequences, namely automatic building extraction with convolutional neural networks (CNNs) and selective real-time depth estimation from aerial imagery. We use transfer learning to train the Faster R-CNN method for real-time deep object detection, by combining a large ground-based dataset for urban scene understanding with a smaller number of images from an aerial dataset. We achieve an average precision (AP) of about 80 % for the task of building extraction on a selected evaluation dataset. Our evaluation focuses on both dataset-specific learning and transfer learning. Furthermore, we present an algorithm that allows for multi-view depth estimation from aerial image sequences in real-time. We adopt the semi-global matching (SGM) optimization strategy to preserve sharp edges at object boundaries. In combination with the Faster R-CNN, it allows a selective reconstruction of buildings, identified with regions of interest (RoIs), from oblique aerial imagery.

Numerical Modelling of Cohesive Bank Migration

River morphological evolution is a challenging topic, involving hydrodynamic flow, sediment transport and bank stability. Lowland rivers are often characterized by the coexistence of granular and cohesive material, with significantly different behaviours. This paper presents a bidimensional morphological model to describe the evolution of the lower course of rivers, where there are both granular and cohesive sediments. The hydrodynamic equations are coupled with two advection–diffusion equations, which consider the transport of granular and cohesive suspended sediment concentration separately. The change of bed height is evaluated as the sum of the contributions of granular and sediment material. A bank failure criterion is developed and incorporated into the numerical simulation of the hydrodynamic flood wave and channel evolution, to describe both bed deformation and bank recession. To this aim, two particular mechanisms are considered: the former being a lateral erosion due to the current flow and consequent cantilever collapse and the latter a geostatic failure due to the submergence. The equation system is integrated by means of a finite volume scheme. The resulting model is applied to the Tagliamento River, in northern Italy, where the meander migration is documented through a sequence of aerial images. The channel evolution is simulated, imposing an equivalent hydrograph consisting of a sequence of flood waves, which represents a medium year, with reference to their effect on sediment transport. The results show that the model adequately describes the general morphological evolution of the meander.

A novel fast target tracking method for UAV aerial image

Abstract Unmanned aerial vehicles (UAV) are able to achieve autonomous flight without drivers, and UAV has been a key tool to extract space data. Therefore, how to detect the trajectories of targets from UAV aerial image sequences is of great importance. Because local features are suitable to detect target tracking, we exploit scaleinvariant feature transform (SIFT) features to describe the interesting keypoints of targets. The main innovation of this paper is to utilize Multiple hypothesis tracking (MHT) algorithm to track an object (target) in a series of image sequences. Particularly, we develop a MHT framework based on a multidimensional assignment formulation and a sliding time window policy. To obtain target tracking from UAV aerial image sequences, three steps should be done, that is, 1) Breaking each track set into tracklet at a specific time, 2) Estimating the association cost of each track set, 3) Merging trajectory fragments to a longer one iteratively. Finally, we collect several UAV aerial image sequences with different target density to construct a dataset, and experimental results demonstrate the effectiveness of the proposed algorithm.