Online changedetection involves monitoring a stream of data for changes in the statistical properties of incoming observations. A good change detector will detect any changes shortly after they occur, while raising f...
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ISBN:
(数字)9781728113982
ISBN:
(纸本)9781728113982
Online changedetection involves monitoring a stream of data for changes in the statistical properties of incoming observations. A good change detector will detect any changes shortly after they occur, while raising few false alarms. Although there are algorithms with confirmed optimality properties for this task, they rely on the exact specifications of the relevant probability distributions and this limits their practicality. In this work we describe a kernel-based variant of the Cumulative Sum (CUSUM) changedetection algorithm that can detect changes under less restrictive assumptions. Instead of using the likelihood ratio, which is a parametric quantity, the Kernel CUSUM (KCUSUM) algorithm compares incoming data with samples from a reference distribution using a statistic based on the Maximum Mean Discrepancy (MMD) non-parametric testing framework. The KCUSUM algorithm is applicable in settings where there is a large amount of background data available and it is desirable to detect a change away from this background setting. Exploiting the random-walk structure of the test statistic, we derive bounds on the performance of the algorithm, including the expected delay and the average time to false alarm.
In a software-defined network (SDN), traffic statis-tics from switches are essential for the controller to satisfy different application requirements (e.g., attack detection, load balancing, etc.). To pursue better me...
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In a software-defined network (SDN), traffic statis-tics from switches are essential for the controller to satisfy different application requirements (e.g., attack detection, load balancing, etc.). To pursue better measurement accuracy, some solutions utilize flow tables to obtain traffic statistics, but the limited TCAM-based flow entries fail to accommodate massive traffic. Another alternative solution is sketch (i.e., a compact data structure), which can be deployed to achieve fine-grained traffic measurement. Nevertheless, traditional sketches (e.g., Count-Min) cannot record flow labels of elephant flows, and meanwhile, sketches that pay excessive attention to the elephant flows in-evitably sacrifice the accuracy of the mouse flows. Consequently, this paper proposes a novel model that combines sketch and flow table for per-flow size measurement in SDNs. The sketch in our model not only separates the mouse and elephant flows but also counts the statistics of mouse flows. Moreover, with the designed algorithm, we take full advantage of precious flow entries to keep elephant flows in the flow table. Simulation experiments based on real-world datasets show that our approach has the best performance in per-flow size estimation, flow size distribution, entropy estimation, heavy hitter detection, and heavy changedetection compared to existing methods.
In recent years, fade detectionalgorithms can classify fade scenes in massive video libraries have been developed. However, these algorithms misclassify some non-fade scenes as fade scenes, especially dissolve scenes...
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In recent years, fade detectionalgorithms can classify fade scenes in massive video libraries have been developed. However, these algorithms misclassify some non-fade scenes as fade scenes, especially dissolve scenes and scenes with captions or flashing light sources. This paper proposes a new fade detection algorithm that uses similarity tendencies of luminance transitions to overcome such obstacles. To prevent detection accuracy degradation by letterboxing and captions, video frames are simplified. Then, fade candidates are detected by transition boundary detection using the angular and curvature characteristics of the luminance vectors. Finally, luminance flipping detection improves the detection accuracy by extracting the luminance retrograde phenomenon that occurs with flashing or light source movements. Through objective evaluation using F-1 score, the detection accuracy of the proposed algorithm was 0.884, which is an increase of 0.187 (21.2% improvement) compared with the average F-1 score of existing high-performance methods.
changedetection is one of the important tasks for video surveillance systems. A variety of learning-based approaches have been proposed, but class imbalance in training data degrades their learning efficiency. In thi...
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ISBN:
(纸本)9781728109909
changedetection is one of the important tasks for video surveillance systems. A variety of learning-based approaches have been proposed, but class imbalance in training data degrades their learning efficiency. In this paper, we propose a cross entropy loss with a modulating term in cosine form to handle this class imbalance. Although the original focal loss focuses only on reducing weights for well-classified data, the proposed function is designed to preserve sufficient gradients for rare hard samples as well. This property allows a network to learn mainly from a few significant samples on which the network should focus. We validate the proposed loss through various experiments on CDNet2014 dataset, and the results show that the network trained with the proposed loss achieves better performance than other state-of-the-arts in various complex scenarios.
We proposed a method to extract causal relations of clusters from multi-dimensional event sequence data, along with another method to detect the changes of the extracted relations over time. The proposed Granger Clust...
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ISBN:
(数字)9781728184708
ISBN:
(纸本)9781728184715
We proposed a method to extract causal relations of clusters from multi-dimensional event sequence data, along with another method to detect the changes of the extracted relations over time. The proposed Granger Cluster Sequence Mining (GCSM) algorithm identifies the pairs of spatial data clusters that have causality over time with each other. It extends the Cluster Sequence Mining algorithm, which utilized a statistical inference technique to identify occurrence relation, with a causality based on Granger causality. We also proposed a statistical model to infer the changes over time of each extracted causal relation. With experiments using synthetic data and semi-real data, we confirmed that the algorithm works correctly, and able to extract the embedded causal relations with high F-score and high accuracy of change points.
Many modern simultaneous localization and mapping (SLAM) techniques rely on sparse landmark-based maps due to their real-time performance. However, these techniques frequently assert that these landmarks are fixed in ...
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ISBN:
(数字)9781728162126
ISBN:
(纸本)9781728162133
Many modern simultaneous localization and mapping (SLAM) techniques rely on sparse landmark-based maps due to their real-time performance. However, these techniques frequently assert that these landmarks are fixed in position over time, known as the static-world assumption. This is rarely, if ever, the case in most real-world environments. Even worse, over long deployments, robots are bound to observe traditionally static landmarks change, for example when an autonomous vehicle encounters a construction zone. This work addresses this challenge, accounting for changes in complex three-dimensional environments with the creation of a probabilistic filter that operates on the features that give rise to landmarks. To accomplish this, landmarks are clustered into cliques and a filter is developed to estimate their persistence jointly among observations of the landmarks in a clique. This filter uses estimated spatial-temporal priors of geometric objects, allowing for dynamic and semi-static objects to be removed from a formally static map. The proposed algorithm is validated in a 3D simulated environment.
Quickest changedetection in a sensor network is considered where each sensor observes a sequence of random variables and transmits its local information on the observations to a fusion center. At an unknown point in ...
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ISBN:
(数字)9781728154787
ISBN:
(纸本)9781728154794
Quickest changedetection in a sensor network is considered where each sensor observes a sequence of random variables and transmits its local information on the observations to a fusion center. At an unknown point in time, the distribution of the observations at all sensors changes. The objective is to detect the change in distribution as soon as possible, subject to a false alarm constraint. We consider minimax formulations for this problem and propose a new approach where transmissions are ordered and halted when sufficient information is accumulated at the fusion center. We show that the proposed approach can achieve the optimal performance equivalent to the centralized cumulative sum (CUSUM) algorithm while requiring fewer sensor transmissions. Numerical results for a shift in mean of independent and identically distributed Gaussian observations show significant communication savings for the case where the change seldom occurs which is frequently true in many important applications.
Anomalous changedetection (ACD) methods separate common, uninteresting changes from rare, significant changes in co-registered images collected at different points in time. In this paper we evaluate methods to improv...
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ISBN:
(数字)9781728157450
ISBN:
(纸本)9781728157467
Anomalous changedetection (ACD) methods separate common, uninteresting changes from rare, significant changes in co-registered images collected at different points in time. In this paper we evaluate methods to improve the performance of ACD in detecting human activity in SAR imagery using outdoor music festivals as a target. Our results show that the low dimensionality of SAR data leads to poor performance of ACD when compared to simpler methods such as image differencing, but augmenting the dimensionality of our input feature space by incorporating local spatial information leads to enhanced performance.
Advancement of Satellite technology gives rapid response to the complex problems and decision making. Due to the impact of climate change, loss of rainfall, drought, risen of sea level are some of the consequences. Re...
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ISBN:
(数字)9781728151977
ISBN:
(纸本)9781728151984
Advancement of Satellite technology gives rapid response to the complex problems and decision making. Due to the impact of climate change, loss of rainfall, drought, risen of sea level are some of the consequences. Recently, the cyclone Fani hits oddisa severely and the livelihood of the people is in standstill. The satellite images of before and after Fani shows the severity of the cyclone. This paper, we introduced three methods which includes Large Scale Mean Shift (LSMS), Multivariate Alteration detection (MAD) and Local statistic Feature extraction methods are used to detect the changes in the heterogeneous images. The satellite images of before and after the cyclone Fani is considered for processing and the severity is estimated. The satellite images are calibrated and preprocessed for removing the speckle noises. With the application of regression prediction algorithm used to tune the accuracy of segmentation and changedetection process. The model is tested with the satellite image and the results are very promising. This approach can also be applied in medical images to detect the changes in features, which helps the physician to diagnose the decease.
This paper presents an efficient GPU-based algorithm to perform histogram analysis of sub-images for changedetection in SAR images. It is designed to use three-stage parallelisms: the SAR images are sub-divided and d...
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ISBN:
(数字)9781728163741
ISBN:
(纸本)9781728163758
This paper presents an efficient GPU-based algorithm to perform histogram analysis of sub-images for changedetection in SAR images. It is designed to use three-stage parallelisms: the SAR images are sub-divided and distributed to all the GPU devices; raster-scans for the sub-divided images are parallelized with many thread-groups inside each GPU; and 32 threads inside the group cooperatively compute the 32 statistical elements one by one by reusing the sub-histograms generated from the overlapped pixels until the dynamic range is modified. The analyzer with quad-GPU takes 1.7 s, which is 5.2 and 57.5 times faster than the conventional analyzer and that with 32-threaded dual-CPU, respectively, when applied to the problem of flood-water detection in ALOS-2 images with 37305×26811 pixels.
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