Real-Time Image Enhancement Using DCT Techniques for Video Surveillance

Kumar, Dileep; Ram, G. Challa; Subbarao, M. V.

doi:10.1007/978-981-16-8554-5_43

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Video monitoring systems serve as an additional layer of security and management, providing an extra set of eyes to protect assets and notify security staff of suspicious or potentially dangerous activities. They are a crucial component of any comprehensive safety strategy [6,9]. These systems utilize various types of video security cameras, including color, analog, digital, monochrome, lowlight level (LLL) amplified, infrared (IR), and others, to cater to different surveillance needs and environments.…”

Section: Video Surveillance Systemmentioning

confidence: 99%

“…With the advancement of video surveillance technology, authorities now have virtual eyes on the ground, and the system can promptly alert them whenever suspicious or terrorist activity is detected, thereby ensuring the safety of facilities and individuals. This technology has become a vital component of comprehensive safety strategies [6,9]. Pan or tilt components can enhance the capabilities of digital camera systems, both for indoor and outdoor surveillance.…”

Section: Video Surveillance Systemmentioning

confidence: 99%

“…Humans use the item in a wide range of situations, such while walking, jogging, in all kinds of weather, etc. [6,8]. The scene's brightness, coherence, polarization, and color all have an impact on the absurdity of nighttime thermal photography [9].…”

Section: Nighttime Enhancementmentioning

confidence: 99%

See 2 more Smart Citations

Recent Trends in Video Surveillance System in Dense Environment: - A Review Paper

Himanshu Tyagi

2023

IJRITCC

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Snow, fog, lightning, torrential rain, and darkness degrade outdoor surveillance footage. The detection, categorization, and event/object recognition capabilities of video surveillance systems in congested environments have attracted considerable interest. Real-time video analysis algorithms in various weather conditions have been enhanced by technology. Other examples include background extraction, the see-through algorithm, deep learning models, CNN for nocturnal incursions, the system for high-quality underwater monitoring utilising optical-wireless video surveillance, LVENet, and edge computing. In the current study, these methodologies improved monitoring efficiency and decreased human error. This study details these video surveillance techniques, platforms, and supplementary materials. After discussing prevalent building and architectural styles briefly, significant system evaluations are presented. This study contrasts current surveillance systems with various methods for real-time video processing under challenging weather conditions in order to provide readers with a thorough understanding of the system. The following research is also highlighted.

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Section: Video Surveillance Systemmentioning

confidence: 99%

Section: Video Surveillance Systemmentioning

confidence: 99%

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Recent Trends in Video Surveillance System in Dense Environment: - A Review Paper

Himanshu Tyagi

2023

IJRITCC

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A Review Paper on Real-Time Video Analysis in Dense Environment for Surveillance System

Tyagi

Kumar

2022

2022 International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP)

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RETRACTED: The reptile optimized deep learning model for land cover classification of the uppal earth region in telangana state using satellite image fusion

Aruna Sri,

Santhi

2024

IFS

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This study addresses challenges in land use and cover identification using remote sensing (RS) imagery, focusing on the Uppal region. By leveraging deep learning models, particularly an optimized ResNext-50 architecture, we aim to enhance efficiency and accuracy in classifying land features. Our approach integrates Landsat-8 and hyper-spectral satellite data, utilizing preprocessing techniques like dark subtraction, stacking, merging, and spectral enhancement. Principal Component Analysis (PCA) is applied to streamline high-dimensional feature sets obtained from pre-processed spectral data. We further employ hybrid NSCT-FDCT fusion for integrating Landsat-8 and hyperspectral images. The resulting fused image is fed into our classification process, utilizing the modified ResNext50 (Deep Learning Architecture) model with Reptile Search Optimization for weight link optimization. Notably, our proposed method achieves impressive outcomes: 97% accuracy, 96% sensitivity, 99% specificity, 3% error, 97% precision, and a 95% Matthew Correlation Coefficient. This demonstrates the efficacy of our approach in predicting diverse land covers within the Uppal region, showcasing the potential of Landsat-8 and Hyper-spectral data for accurate land use and cover identification.

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Real-Time Image Enhancement Using DCT Techniques for Video Surveillance

Cited by 3 publications

References 10 publications

Recent Trends in Video Surveillance System in Dense Environment: - A Review Paper

Recent Trends in Video Surveillance System in Dense Environment: - A Review Paper

A Review Paper on Real-Time Video Analysis in Dense Environment for Surveillance System

RETRACTED: The reptile optimized deep learning model for land cover classification of the uppal earth region in telangana state using satellite image fusion

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