Video Compression Algorithm Based on Frame Difference Approaches

Al-Ani, MuzhirShaban; Hammouri, Ali

doi:10.5121/ijsc.2011.2407

Cited by 17 publications

(4 citation statements)

References 15 publications

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“…Pasandi and Nadeem [15] proposed to treat all network cameras as a whole and use the temporal and spatial correlation between cameras to eliminate redundant frames. Al-Ani and Hammouri [16] proposed a method based on frame difference to remove video redundant frames, deleting any frame between consecutive frames that is lower than the average value of the difference between frames. The above methods use the spatio-temporal characteristics between video frames for redundant frame removal and achieve better results, but these methods do not take into account the structural similarity between video frames, and some redundant frames still exist.…”

Section: Related Workmentioning

confidence: 99%

Edge Computing-Based Vehicle Detection in Intelligent Transportation Systems

Pan,

Guan,

Zhao

et al. 2023

cai

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Vehicle detection in intelligent transportation systems usually adopts cloud computing mode. The increasing amount of traffic surveillance video has brought challenges to the storage, communication, and processing of intelligent transportation systems based on cloud computing models. In this paper, we propose a vehicle detection scheme based on edge computing. First, the traffic surveillance video is preprocessed at the edge device. Using the frame difference algorithm based on structural similarity (SSIM) to remove video redundant frames, and avoid repeated frames in the subsequent extracted key frame sequence. Then, a frame difference algorithm based on local maxima is used to extract key frames as the basis for subsequent vehicle detection. Finally, the YOLOv5s is improved and used for vehicle detection. The efficient channel attention mechanism (ECA) is introduced to enhance the important features of the vehicle and suppress the general features to strengthen the detection network's ability to identify vehicle targets. At the same time, the Focal loss function is introduced to solve the positive and negative sample imbalance problem and improve the detection speed. The experimental results show that the scheme has more advantages than the original YOLOv5s in terms of precision, recall, and mean average precision.

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Section: Related Workmentioning

confidence: 99%

Edge Computing-Based Vehicle Detection in Intelligent Transportation Systems

Pan,

Guan,

Zhao

et al. 2023

cai

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“…In [12] A. used Discrete Wavelet Transform (DWT) to compress the video as it was being sent or stored over the network. The results acquired decreased the size of the movie and the transmission time.…”

Section: Related Workmentioning

confidence: 99%

A Review for Video Authentication and Integrity

Nazar¹,

Hasso²

2022

Technium

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In daily life, digital video authentication is a crucial problem. Digital videos may be recorded or captured by many different devices, and these can all be sent through the internet and other unsecured means. Due to the advancement of video editing tools, there is an issue with the editing or changing of digital video without authorization. There are many different methods and techniques, which are suggested for overcoming these problems. Digital video is the fundamental of surveillance system. However, it is easy to be modified by threats that may decrease drastically the trust of surveillance system. The techniques used for video authentication by various authors are discussed in this study.

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“…Various approaches have been developed to address the above-mentioned challenges to optimize storage requirements and facilitate the analysis of CCTV footage. However, the existing solutions rely on traditional methods, such as video compression [2], timed deletion [3], and manual intervention, none of which are efficient when it comes to the 21st century's technology standards. This paper presents an efficient solution for the storage and analysis of CCTV footage through motion-based frame removal [4].…”

Section: Introductionmentioning

confidence: 99%

Efficient Storage and Analysis of Videos through Motion-Based Frame Removal

Anderson

2023

Preprint

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The storage and analysis of CCTV footage pose a significant challenge in modern surveillance systems. This paper presents an innovative and modern solution for efficient storage optimization and analysis through motion-based frame removal techniques. By leveraging the OpenCV library redundant frames are removed, resulting in substantial storage reduction without losing important motion events. The methodology uses Python’s OpenCV library with optional technologies such as Django, SQLite, and Bootstrap, ensuring reliability, scalability, usability, and effective data visualisation. Experimental results have demonstrated the efficacy across different camera setups in different environments and their ability to preserve critical information. The approach offers a practical solution for decreasing cost, improving analysis efficiency, and enhancing usability. This research contributes to the field of CCTV surveillance by providing a practical and industry-oriented solution. The findings inspire further improvements in storage optimization and pave the way for more intelligent surveillance systems.

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Video Compression Algorithm Based on Frame Difference Approaches

Cited by 17 publications

References 15 publications

Edge Computing-Based Vehicle Detection in Intelligent Transportation Systems

Edge Computing-Based Vehicle Detection in Intelligent Transportation Systems

A Review for Video Authentication and Integrity

Efficient Storage and Analysis of Videos through Motion-Based Frame Removal

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