The rapid evolution of urban surveillance systems has created an urgent need for advanced anomaly detection methods capable of interpreting complex public environments. This study employs the Preferred Reporting Items...
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ISBN:
(数字)9798331532970
ISBN:
(纸本)9798331532987
The rapid evolution of urban surveillance systems has created an urgent need for advanced anomaly detection methods capable of interpreting complex public environments. This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to evaluate deep learning's (DL) role in video-based anomaly detection. It contrasts conventional approaches with cutting-edge architectures like spatiotemporal convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based models. Our analysis demonstrates DL's superior performance over traditional methods across multiple benchmarks while revealing significant implementation challenges in real-world deployment, including computational complexity, cross-domain generalization, and ethical constraints. The study provides a comprehensive taxonomy of anomaly types, examines key evaluation metrics for operational systems, and identifies emerging solutions like edge-compatible lightweight models and privacy-preserving federated learning. By synthesizing a decade of research progress and practical limitations, this work offers actionable insights for developing robust, efficient, and socially responsible surveillance systems. The study proposes future directions focused on self-supervised learning, multimodal sensorfusion, and explainable AI frameworks to address critical gaps in urban security applications.
Modem surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets, In order to generate target tracks and estimates, the s...
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Modem surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets, In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates, The information graph is introduced as a way of modeling information flow is distributed fusion systems and for developing algorithms. fusion for target tracking involves two main operations: estimation and association. Distributed estimation algorithms based on the information graph are presented for arbitrary fusionarchitectures and related to linear and nonlinear distributed estimation results. The distributed data association problem is discussed in terms of track-to-track association likelihoods. Distributed versions of two popular tracking approaches (joint probabilistic data association and multiple hypothesis tracking) are then presented, and examples of applications are given.
The next decade will require the development of complex sensor systems that integrate data from a large number of sensor elements. Such systems will play important roles in a wide variety of industrial and defense sys...
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ISBN:
(纸本)0819428256
The next decade will require the development of complex sensor systems that integrate data from a large number of sensor elements. Such systems will play important roles in a wide variety of industrial and defense systems, as the fusion of multiple sources of information is crucial to sensor operation in noisy environments, and in complex decision making. The arrival of ubiquitous processing elements is one requirement for the development of such systems;however, the ability to connect and integrate these elements at the logical level is the more limiting aspect of their development. Furthermore, it is unlikely that such systems can be developed in a single linear process. It is much more probable that such systems will need to be evolved over time, perhaps a substantial period of time, and as result the ability to logically interconnect heterogeneous elements in an evolutionary manner will be of great importance. This paper outlines some approaches to this problem based on the distributed object-computing model as introduced in the OMG CORBA. It is our belief that this technology is maturing to the point that it could form the foundations for a sensor architecture that would support the evolutionary development of complex sensor networks.
In this paper, we proposed a maximum likelihood fusion approach for multisensorfusionapplications. The proposed approach was based on a parametric modeling of the noise covariance and formulated in the transformed n...
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ISBN:
(纸本)0819428256
In this paper, we proposed a maximum likelihood fusion approach for multisensorfusionapplications. The proposed approach was based on a parametric modeling of the noise covariance and formulated in the transformed noise subspace. It could solve the fusion problems when the sensor noises are correlated and the scaling coefficients unknown. The approach could also deal with nonstationary signals. We showed that in the optimization process, the computation of the noise parameters and the scaling coefficients were separable leading to a reduced optimization dimensionality and computational complexity. Computer simulations were used to demonstrate the effectiveness of the proposed fusion approach.
Using Bayesian statistical methods a formulation is setup for fusing multi band data from LWIR sensors. This formulation is illustrated with applications to synthetic data consisting of 100 signatures in the wavelengt...
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ISBN:
(纸本)0819428256
Using Bayesian statistical methods a formulation is setup for fusing multi band data from LWIR sensors. This formulation is illustrated with applications to synthetic data consisting of 100 signatures in the wavelength bands 6-10 mu m, 11-16 mu m and 17-21 mu m. Following the works of Jaynes, and Bretthorst, a Bayesian formulation is given for detrending the time seriesdata for the emissive area, followed by estimations of frequencies and their amplitudes. This formulation is illustrated with analysis of the synthetic data.
The visualization of a scene in murky atmospheric conditions is improved by fusing multiple images. A key feature of this system is the use of the wavelet domain in the fusion process. Many possible fusion formulas in...
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ISBN:
(纸本)0819428256
The visualization of a scene in murky atmospheric conditions is improved by fusing multiple images. A key feature of this system is the use of the wavelet domain in the fusion process. Many possible fusion formulas in this domain exist and to find the "best" formula, we formulate an optimization problem. We assume a set of training data consisting of a sequence of images with the presence of atmospheric effects and the corresponding image with no atmospheric effects present (ground truth). Next, we perform a search over the parameter space of our "generic fusion formula" attempting to minimize the error between the original ground truth image and the image created by fusing the noisy data. Using the resulting "best" fusion formula, we have created a system for pixel level fusion. Experimental results are shown and discussed. Possible applications of this system include processing of outdoor security system data, filters for outdoor vehicle image data and use in heads-up displays.
The Multi-sensorfusion Management (MSFM) algorithm is extended to admit a richer variety of behavior. More realistic sensor characteristic models are used such as detection-plus-bearing sensors and false alarm probab...
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The Multi-sensorfusion Management (MSFM) algorithm is extended to admit a richer variety of behavior. More realistic sensor characteristic models are used such as detection-plus-bearing sensors and false alarm probabilities commensurate with actual sonar sensor systems. The performance of the modified MSFM algorithm is illustrated on a realistic anti-submarine warfare (ASW) application.
We discuss Virtual Associative Networks (VANs) and their relevance for addressing computationally prohibitive sensorfusion problems (with results in Dynamic sensor Management). To our knowledge, this discussion of VA...
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We discuss Virtual Associative Networks (VANs) and their relevance for addressing computationally prohibitive sensorfusion problems (with results in Dynamic sensor Management). To our knowledge, this discussion of VAN technology for sensorfusion is unique and our current result involving VANs for Dynamic sensor Management is the first of its kind. The following provides methodology, results, and extensions.
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