Visual Attention and Background Subtraction With Adaptive Weight for Hyperspectral Anomaly Detection

Xiang, Pei; Song, Jiangluqi; Qin, Hanlin; Tan, Wei; Li, Huan; Zhou, Huixin

doi:10.1109/jstars.2021.3052968

Cited by 27 publications

(7 citation statements)

References 48 publications

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“…Skeleton-based action recognition systems often use hierarchical GCN, which may result in joint feature information loss after lengthy diffusion. To increase the local context information of joints, the author [40] offers multiscale mixed dense graph CNN. Two modules, spatial and attention, are used to fine-tune the spatial-temporal aspects.…”

Section: Graph-based Network Object Detectionmentioning

confidence: 99%

Secure Data Deduplication System with Cyber Security Multikey Management in Cloud Storage

Kumar¹,

Sathiya²,

Mohanta

et al. 2022

Security and Communication Networks

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Object detection is essential in a video surveillance system. To recognize an item in a movie, we first examine each picture pixel by pixel. The Secure Data Deduplication system in segmentation is the process of separating distinct picture components into pixels in digital image processing. The performance of segmentation is influenced by irregular and/or poor lighting. These characteristics have a significant impact on the video surveillance system’s real-time object detection process. A multikey management system based on a modified ResNet model is presented in this research (M-ResNet). Cyber security is a suggested algorithm application that is used to improve images that are influenced by a lack of light. The experimental findings reveal a significant improvement in detecting objects in the video stream as compared to the present technique output and modification architecture of the ResNet model. The suggested model achieves superior results in measures like precision, recall, and pixel accuracy, as well as a decent increase in object recognition.

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Section: Graph-based Network Object Detectionmentioning

confidence: 99%

Secure Data Deduplication System with Cyber Security Multikey Management in Cloud Storage

Kumar¹,

Sathiya²,

Mohanta

et al. 2022

Security and Communication Networks

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“…An adaptive least mean square technique can be used to update the dynamic features of the background. The fuzzy histogram describes the temporal properties of the pixels by utilising the fuzzy C means clustering with fuzzy nearness degree (FCFN) [37] background subtraction method. It overcomes categorization challenges by classifying things in the background and foreground.…”

Section: Object Detection Using Background Subtraction Methodsmentioning

confidence: 99%

Realtime Object Detection Through M-ResNet in Video Surveillance System

Prabu¹,

Gnanasekar²

2023

Intelligent Automation &Amp; Soft Computing

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Object detection plays a vital role in the video surveillance systems. To enhance security, surveillance cameras are now installed in public areas such as traffic signals, roadways, retail malls, train stations, and banks. However, monitoring the video continually at a quicker pace is a challenging job. As a consequence, security cameras are useless and need human monitoring. The primary difficulty with video surveillance is identifying abnormalities such as thefts, accidents, crimes, or other unlawful actions. The anomalous action does not occur at a higher rate than usual occurrences. To detect the object in a video, first we analyze the images pixel by pixel. In digital image processing, segmentation is the process of segregating the individual image parts into pixels. The performance of segmentation is affected by irregular illumination and/or low illumination. These factors highly affect the real-time object detection process in the video surveillance system. In this paper, a modified ResNet model (M-Resnet) is proposed to enhance the image which is affected by insufficient light. Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream. The proposed model shows better results in the metrics like precision, recall, pixel accuracy, etc., and finds a reasonable improvement in the object detection.

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“…Moreover, attention mechanisms have been extensively applied in the domain of computer vision [32][33][34], thus contributing to the state-of-the-art HSI processing. The attention mechanism has been broadly applied for HSI classification, semantic segmentation, pan sharpening, object detection, and change detection [35][36][37][38][39]. In recent years, attention mechanisms have also been applied in the image quality improvement domain.…”

Section: Data-driven Destriping Methodsmentioning

confidence: 99%

Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks

Wang

Jiang

et al. 2022

Remote Sensing

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In hyperspectral imaging (HSI), stripe noise is one of the most common noise types that adversely affects its application. Convolutional neural networks (CNNs) have contributed to state-of-the-art performance in HSI destriping given their powerful feature extraction and learning capabilities. However, it is difficult to obtain paired training samples for real data. Most CNN destriping methods construct a paired training dataset with simulated stripe noise for network training. However, when the stripe noise of real data is complex, destriping performance of the model is constrained. To solve this problem, this study proposes a real HSI stripe removal method using a toward real HSI stripe removal via direction constraint hierarchical feature cascade network (TRS-DCHC). TRS-DCHC uses the stripe noise extract subnetwork to extract stripe patterns from real stripe-containing HSI data and incorporates clean images to form paired training samples. The destriping subnetwork advantageously utilizes a wavelet transform to explicitly decompose stripe and stripe-free components. It also adopts multi-scale feature dense connections and feature fusion to enrich feature information and deeply mine the discriminate features of stripe and stripe-free components. Our experiments on both simulated and real data of various loads showed that TRS-DCHC features better performance in both simulated and real data compared with state-of-the-art method.

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Visual Attention and Background Subtraction With Adaptive Weight for Hyperspectral Anomaly Detection

Cited by 27 publications

References 48 publications

Secure Data Deduplication System with Cyber Security Multikey Management in Cloud Storage

Secure Data Deduplication System with Cyber Security Multikey Management in Cloud Storage

Realtime Object Detection Through M-ResNet in Video Surveillance System

Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks

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