A scene image classification technique for a ubiquitous visual surveillance system

Kaljahi, Maryam Asadzadeh; Palaiahnakote, Shivakumara; Anisi, Mohammad Hossein; Idris, Mohd Yamani Idna; Blumenstein, Michael; Khan, Muhammad Khurram

doi:10.1007/s11042-018-6151-x

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…While security systems employing computer vision algorithms and machine learning techniques have significantly improved the surveillance landscape, the role of CCTV operators remains crucial in maintaining a comprehensive and effective surveillance system. Numerous studies have demonstrated the efficacy of diverse approaches for the automated identification of potential hazards, enabling operators to respond appropriately and make decisions, particularly in intricate scenarios [24]. Despite their merits, relying solely on fully automated mechanisms for threat detection may not be advisable, as they could potentially overlook genuinely perilous incidents or generate unwarranted false alarms [25].…”

Section: Vision-basedmentioning

confidence: 99%

Advancements in Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-the-Art

Ameen¹,

Stone²

2023

IJNSA

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Growing apprehensions surrounding public safety have captured the attention of numerous governments and security agencies across the globe. These entities are increasingly acknowledging the imperative need for reliable and secure crowd-monitoring systems to address these concerns. Effectively managing human gatherings necessitates proactive measures to prevent unforeseen events or complications, ensuring a safe and well-coordinated environment. The scarcity of research focusing on crowd monitoring systems and their security implications has given rise to a burgeoning area of investigation, exploring potential approaches to safeguard human congregations effectively. Crowd monitoring systems depend on a bifurcated approach, encompassing vision-based and non-vision-based technologies. An in-depth analysis of these two methodologies will be conducted in this research. The efficacy of these approaches is contingent upon the specific environment and temporal context in which they are deployed, as they each offer distinct advantages. This paper endeavors to present an in-depth analysis of the recent incorporation of artificial intelligence (AI) algorithms and models into automated systems, emphasizing their contemporary applications and effectiveness in various contexts.

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Section: Vision-basedmentioning

confidence: 99%

Advancements in Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-the-Art

Ameen¹,

Stone²

2023

IJNSA

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“…By acquiring features from images via learning, as opposed to manual design, the potential to obtain more discriminative features arises, which aligns more fittingly with the current problem. Classical unsupervised-feature-learning-based methods include and are not limited to principal component analysis (PCA) [25]- [31], k-means clustering [32]- [37], sparse coding [38]- [42], and autoencoders [43] [44]. Chaib et al [45] employed scaleinvariant feature transformation and robust feature operators to extract local features from satellite imagery.…”

mentioning

confidence: 99%

Large Kernel Separable Mixed ConvNet for Remote Sensing Scene Classification

Zhang,

Cui,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Among tasks related to intelligent interpretation of remote sensing data, scene classification mainly focuses on the holistic information of the entire scene. Compared to pixel-level or object-based tasks, it involves a richer semantic context, making it more challenging. With the rapid advancement of deep learning, convolutional neural networks (CNNs) have found widespread applications across various domains, and some work has introduced them into scene classification tasks. However, traditional convolution operations involve sliding small convolutional kernels across an image, primarily focusing on local details within a small receptive field. To achieve better modeling of the entire image, the smaller receptive field limits the ability of convolution operation to capture features over a broader range. To this end, we introduce a large kernel CNNs into the scene classification task to expand the receptive field of the mode, which allows us to capture comprehensive non-local information while still acquiring rich local details. However, in addition to encoding spatial association, the effective information within the feature maps is also strongly channel-related. Therefore, to fully model this channel dependency, a novel channel separation and mixing module has been designed to realize feature correlation in the channel dimension. The combination of them forms a Large Kernel Separable Mixed Convnet (LSMNet), enabling the model to capture effective dependencies of feature maps in both spatial and channel dimensions, thus achieving enhanced feature expression. Extensive experiments conducted on three datasets have also validated the effectiveness of the proposed method.

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