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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Yoshitaro Yonamoto Kai Morino Kenji Yamanishi Graduate School of Information Science & Technology The University of Tokyo Tokyo Japan

ISBN: (纸本)9781509052073

In this paper, we propose a novel change detection method for temporal networks. In usual change detection algorithms, change scores are generated from an observed time series. When this change score reaches a threshold, an alert is raised to declare the change. Our method aggregates these change scores and alerts based on network centralities. Many types of changes in a network can be discovered from changes to the network structure. Thus, nodes and links should be monitored in order to recognize changes. However, it is difficult to focus on the appropriate nodes and links when there is little information regarding the dataset. Network centrality such as PageRank measures the importance of nodes in a network based on certain criteria. Therefore, it is natural to apply network centralities in order to improve the accuracy of change detection methods. Our analysis reveals how and when network centrality works well in terms of change detection. Based on this understanding, we propose an aggregating algorithm that emphasizes the appropriate network centralities. Our evaluation of the proposed aggregation algorithm showed highly accurate predictions for an artificial dataset and two real datasets. Our method contributes to extending the field of change detection in temporal networks by utilizing network centralities.

关键词： Time series analysis Games Knowledge engineering Eigenvalues and eigenfunctions Aggregates change detection algorithms Prediction algorithms

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Segmentation-Based Online change detection for Mobile Robots

Segmentation-Based Online Change Detection for Mobile Robots

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2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Bradford Neuman Boris Sofman Anthony Stentz J. Andrew Bagnell Robotics Institute Carnegie Mellon University Pittsburgh PA 15213

The high cost of damaging an expensive robot or injuring people or equipment in its environment make even rare failures unacceptable in many mobile robot applications. Often the objects that pose the highest risk for a mobile robot are those that were not present throughout previous successful traversals of an environment. change detection, a closely related problem to novelty detection, is therefore of high importance to many mobile robotic applications that require a robot to operate repeatedly in the same environment. We present a novel algorithm for performing online change detection based on a previously developed robust online novelty detection system that uses a learned lower-dimensional representation of the feature space to perform measures of *** then further improve this change detection system by incorporating online scene segmentation to better utilize contextual information in the environment. We validate these approaches through extensive experiments onboard a large outdoor mobile robot. Our results show that our approaches are robust to noisy sensor data and moderate registration errors and maintain their performance across diverse natural environments and conditions.

关键词： Feature extraction change detection algorithms Optimization Mobile robots Mobile communication Robustness

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Semi-Supervised Deep Learning for change detection in Agricultural Fields Using Sentinel-2 Imagery

Semi-Supervised Deep Learning for Change Detection in Agricu...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Iason Tsardanidis Charalampos Kontoes BEYOND EO Centre IAASARS National Observatory of Athens Athens Greece

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This paper introduces an original application for detecting changes related to diverse agricultural activities through the analysis of bitemporal Sentinel-2 satellite imagery. Operating without pre-existing samples, our approach generates pseudo-labels using common rule-based Earth Observation (EO) algorithms to identify cases of abrupt loss of vegetation in pairs of consecutive cloud-free images. These artificially generated samples form the basis for training several state-of-the-art change detection (CD) methods. Evaluation on a small ground truth sample, annotated through photo-interpretation by experts, demonstrates our semi-supervised methodology’s high predictive accuracy for agricultural events detection across diverse terrains and cropping practices (i.e., mowing, grazing, harvest, plowing, stubble burning, etc.). The proposed implementation offers a cost-effective, scalable solution for real-time monitoring, providing valuable insights for agricultural activity and facilitating informed decision-making in farm management and biodiversity strategies.

关键词： Training Accuracy change detection algorithms Computational modeling Biological system modeling Decision making Vegetation mapping

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HSMD: An Object Motion detection Algorithm Using a Hybrid Spiking Neural Network Architecture

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IEEE ACCESS 2021年 9卷 125258-125268页

作者： Machado, Pedro Oikonomou, Andreas Ferreira, Joao Filipe Mcginnity, T. Martin Nottingham Trent Univ Sch Sci & Technol Computat Neurosci & Cognit Robot Grp ISTeC03 Clifton Campus Nottingham NG11 8NS England Ulster Univ Intelligent Syst Res Ctr MS225 Magee Campus Derry BT48 7JL Londonderry North Ireland

The detection of moving objects is a trivial task performed by vertebrate retinas, yet a complex computer vision task. Object-motion-sensitive ganglion cells (OMS-GC) are specialised cells in the retina that sense moving objects. OMS-GC take as input continuous signals and produce spike patterns as output, that are transmitted to the Visual Cortex via the optic nerve. The Hybrid Sensitive Motion Detector (HSMD) algorithm proposed in this work enhances the GSOC dynamic background subtraction (DBS) algorithm with a customised 3-layer spiking neural network (SNN) that outputs spiking responses akin to the OMS-GC. The algorithm was compared against existing background subtraction (BS) approaches, available on the OpenCV library, specifically on the 2012 change detection (CDnet2012) and the 2014 change detection (CDnet2014) benchmark datasets. The results show that the HSMD was ranked overall first among the competing approaches and has performed better than all the other algorithms on four of the categories across all the eight test metrics. Furthermore, the HSMD proposed in this paper is the first to use an SNN to enhance an existing state of the art DBS (GSOC) algorithm and the results demonstrate that the SNN provides near real-time performance in realistic applications.

关键词： Retina Motion detection Cameras Heuristic algorithms Mathematical model Computational modeling change detection algorithms SNN HMSD retinal cells object motion sensitive ganglion cells background subtraction object motion detection

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Track-before-detect procedures for early detection of moving target from airborne radars

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2005年第3期41卷 937-954页

作者： Buzzi, S Lops, M Venturino, L Università degli Studi di Cassino Italy

In this paper we present a family of track-before-detect (TBD). procedures for early detection of moving targets from airborne radars. Upon a sectorization of the coverage area, the received echoes are jointly processed in the azimuth-range-Doppler domain and in the time domain through a Viterbi-like algorithm that exploits the physically admissible target transitions between successive illuminations, in order to collect all of the energy back-scattered during the time on target (TOT). A reduced-complexity implementation is derived assuming, at the design stage, that the target does not change resolution cell during the TOT in each scan. The constant false alarm rate (CFAR) constraint is also englobed in the proposed procedures as well as the possibility of working with quantized data. Simulation results show that the proposed algorithms have good detection and tracking capabilities even for high target velocities and low quantization rates.

关键词： Radar tracking Target tracking Radar detection Airborne radar Optical filters Laser radar Optical sensors Radar applications change detection algorithms Object detection

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Extracting Moving Objects More Accurately: A CDA Contour Optimizer

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第12期31卷 4840-4849页

作者： Gao, Fei Li, Yunyang Lu, Shufang Zhejiang Univ Technol Coll Comp Sci & Technol Lab Graph & Image Proc Hangzhou 310023 Peoples R China

In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing algorithm for change detection is proposed. We believe that some problems cannot be avoided in the area of change detection such as 1) region of moving object generated by change detection is slightly larger than the ground-truth and 2) there are always some disjoint and small regions that are independent from the moving objects. To address the problem, our method can optimize the change detection algorithm bases on the idea of edge detection, which can remove the wrong edge or pixel. In the experiments, more than 20 change detection algorithms that include the best algorithm in *** are selected. Most of these change detection algorithms are optimized by the proposed method on PWC, Precision, and FMeasure, where, our optimized algorithm named FgSegNet_v2 is better than all other algorithms in the CDnet. The best-optimized margin of PWC is 0.64, and the fast speed is 548FPS on CPU. Our approach can better resolve the afore-mentioned problems that cannot be avoided and is general and fast. The experiments can be reproduced with C++ on Github https://***/walty19950301/CDA-contour-optimizer.

关键词： change detection algorithms Image edge detection Feature extraction Unsupervised learning Supervised learning Semantics Optical filters Post-processing algorithm optimization method change detection algorithm edge detection motion detection

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Reinforcement Learning Design for Quickest change detection

Reinforcement Learning Design for Quickest Change Detection

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IEEE Conference on Decision and Control

作者： Austin Cooper Sean Meyn Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The field of quickest change detection (QCD) concerns design and analysis of algorithms to estimate in real time the time at which an important event takes place, and identify properties of the post-change behavior. It is shown in this paper that approaches based on reinforcement learning (RL) can be adapted based on any “surrogate information state” that is adapted to the observations. Hence we are left to choose both the surrogate information state process and the algorithm. For the former, it is argued that there are many choices available, based on a rich theory of asymptotic statistics for QCD. Two approaches to RL design are considered: (i) Stochastic gradient descent based on an actor-critic formulation. Theory is largely complete for this approach: the algorithm is unbiased, and will converge to a local minimum. However, it is shown that variance of stochastic gradients can be very large, necessitating the need for commensurately long run times. (ii) Q-learning algorithms based on a version of the projected Bellman equation. It is shown that the algorithm is stable, in the sense of bounded sample paths, and that a solution to the projected Bellman equation exists under mild conditions. Numerical experiments illustrate these findings, and provide a roadmap for algorithm design in more general settings.

关键词： Q-learning Power transmission lines change detection algorithms Real-time systems Testing

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Data Augmentation Using Image Generation for change detection

Data Augmentation Using Image Generation for Change Detectio...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Xuan Li Haibin Duan Hui Zhang Fei-Yue Wang Peng Cheng Laboratory Shenzhen China Beihang University Beijing China Beijing Jiaotong University Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Learning-based change detection (CD) in water scenarios is a key functionality for unmanned aerial vehicle (UAV). However, computer vision algorithms require large number of labeled datasets. Inspired by parallel intelligence, we propose a systematic framework for data generation. In this work, the framework consists of simulated scene and image generation network. In simulated scene, simulated images with pixel-level annotations are automatically generated. Then, image generation network uses paired images (real and simulated) to generate synthetic images. We use simulated and synthetic images in combination with publicly available real-world images to conduct experiments. The experimental results indicate that: 1) simulated images can be used in change detection research; 2) synthetic images effectively improve the performance of supervised change detection model.

关键词： Training Computer vision Systematics Image synthesis change detection algorithms Annotations Digital twin

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Energy efficient change detection over a MAC using physical layer fusion

Energy efficient change detection over a MAC using physical ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Taposh Banerjee Veeraruna Kavitha Vinod Sharma Department of E.C.E Indian Institute of Science Bangalore India

We propose a simple and energy efficient distributed change detection scheme for sensor networks based on Page's parametric CUSUM algorithm. The sensor observations are IID over time and across the sensors conditioned on the change variable. Each sensor runs CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a multiple access channel (MAC) corrupted with IID noise. The fusion center which is the global decision maker, performs another CUSUM to detect the change. We provide the analysis and simulation results for our scheme and compare the performance with an existing scheme which ensures energy efficiency via optimal power selection.

关键词： Energy efficiency Physical layer Sensor fusion change detection algorithms Delay Monitoring Intrusion detection Bayesian methods Testing Fuses

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Large scale change detection techniques dedicated to flood monitoring using ENVISAT wide swath mode data

Large scale change detection techniques dedicated to flood m...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： R. Andreoli H. Yesou Sertit Université Louis Pasteur Illkirch-Graffenstaden France

Semi-automatic flood extraction procedures based on change detection techniques appear particularly adapted to plain flood monitoring and mapping. The change detector was specifically elaborated to analyse ENVISAT ASAR wide swath mode data pairs, which appear very well adapted to flood monitoring over wide areas. The algorithm is based on two analysis levels: an enhanced ratio for strong changes over large homogeneous and flat areas combined with a ratio calculated from the two raw images which aims to keep the raw data's thematic precision. Slope and aspect effects are also eliminated by the use of a digital elevation model during the processing. This change detection analysis was performed on 35 data pairs acquired within the framework of the flood DRAGON project. The first set of results is very promising and robust using HH polarization. change detection between data with W polarization or with different polarization (HH versus W) has to be fully validated but results are promising.

关键词： Large-scale systems Floods Monitoring Polarization Data mining Detectors change detection algorithms Image analysis Algorithm design and analysis Digital elevation models

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