Understanding New Age of Intelligent Video Surveillance and Deeper Analysis on Deep Learning Techniques for Object Tracking

Nagrath, Preeti; Thakur, Narina; Jain, Rachna; Saini, Dharmender; Sharma, Nitika; Hemanth, D. Jude

doi:10.1007/978-3-030-89554-9_2

Cited by 5 publications

(2 citation statements)

References 93 publications

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“…For example, (Pichler & Stehrer, 2021) classified ICT users based on their job roles, such as high-level government officials and legislators, firm directors, commercial and administrative leaders, production supervisors, etc. Similarly, other studies have discovered that the adoption of ICT equipment such as computers, the Internet (including social media), mobile phones, closed circuit television (CCTV), security cameras, information extraction, spy satellites and Internet protocol gadgets help in crime reduction (Arisukwu et al, 2020;Laufs & Borrion, 2022;Nagrath et al, 2022;Owan & Ekpenyong, 2022). Software development with systems administration analysis was the focus of the unique user characteristics in this case.…”

Section: Introductionmentioning

confidence: 97%

Latent Class Analysis of Postgraduate Students’ Behavioral Characteristics toward ICT Use: What Are Their Job Creation Differences?

Owan¹,

Akpan²,

Ekpenyong³

et al. 2022

The International Journal of Adult, Community and Professional

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This study analysed the behavioural characteristics of ICT users among postgraduate students leveraging the Latent Class Analysis (LCA). The study, anchored on the Planned Behaviour Theory, followed the exploratory research design. It adopted the cluster random sampling technique in selecting 1,023 respondents from a population of 2,923 postgraduate students in four federal universities in South-South Nigeria. "Behavioural Characteristics and Job Creation Questionnaire (BCJCQ)", developed by the researchers, was used for data collection. Upon data collection and LCA analysis, the five-class solution was accepted as the best fitting model, based on statistical fit indicators (such as AIC, BIC, entropy, Gsq, and Chsq) and theoretical grounds. Consequently, five classes of behavioural ICT users were identified and named based on their item-response probability, conditional on class. The five classes were named Trendy, Outmoded, Pragmatic, Disciplined and Social users of ICT, with their unique characteristics discussed. The study tested for job creation differences among the classes using a one-way ANOVA and found a significant difference. On average, pragmatic users of ICT created more jobs than social, disciplined, and outmoded users. Trendy users were, on average, the minor job-creating class of ICT users. The study compared the bivariate differences in job creation among the classes using the Tukey HSD test of multiple pairwise comparisons. Based on the results obtained, discussions were made with implications for further research in the evolving area of LCA.

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Section: Introductionmentioning

confidence: 97%

Latent Class Analysis of Postgraduate Students’ Behavioral Characteristics toward ICT Use: What Are Their Job Creation Differences?

Owan¹,

Akpan²,

Ekpenyong³

et al. 2022

The International Journal of Adult, Community and Professional

View full text Add to dashboard Cite

show abstract

“…ML has played a pivotal role in revolutionizing how objects are identified and tracked in video streams, providing enhanced security, safety, and situational awareness in various domains such as public spaces, transportation, and industrial environments. One subset of ML, known as deep learning (DL) [2], has improved video surveillance capabilities by achieving greater accuracy because of its ability to more accurately learn from enormous data patterns using neural networks (NNs) [3], as opposed to ML [4]. DL models have proven to be highly effective at automatically analyzing video data, enabling the detection of objects of interest, and monitoring their movements with unprecedented accuracy [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework

Bazarov Ravshan Ugli,

Mohammed,

et al. 2024

Sensors

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Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains.

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Covid-19 Face Mask Detection Using Deep Learning Techniques

Pushpavathi

Narasimman

2022

Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases

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Understanding New Age of Intelligent Video Surveillance and Deeper Analysis on Deep Learning Techniques for Object Tracking

Cited by 5 publications

References 93 publications

Latent Class Analysis of Postgraduate Students’ Behavioral Characteristics toward ICT Use: What Are Their Job Creation Differences?

Latent Class Analysis of Postgraduate Students’ Behavioral Characteristics toward ICT Use: What Are Their Job Creation Differences?

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework

Covid-19 Face Mask Detection Using Deep Learning Techniques

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