RETRACTED: Adaptive motion estimation and sequential outline separation based moving object detection in video surveillance system

Thenmozhi, T.; Kalpana, A. M.

doi:10.1016/j.micpro.2020.103084

Cited by 24 publications

(4 citation statements)

References 8 publications

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“…Some studies have used a combination of traditional computer vision algorithms [75]. In [76], the adaptive motion estimation segmentation (AMES) and the proposed sequential outline separation (SOS) methods were used to detect multiple moving objects. AMES is a computer vision technique that analyzes the motion information between consecutive frames and then uses that information to separate moving objects from the background.…”

Section: Motion-based Object Detectionmentioning

confidence: 99%

“…Abnormal action detection in HAR refers to the task of identifying actions or behaviors that deviate from what is considered normal or expected. Abnormal action detection focuses on identifying actions that are uncommon, unusual, or potentially dangerous, which include falling, stumbling, abnormal body movements, sudden changes in speed or direction, and actions that are out of context in a given environment [76].…”

Section: Abnormal Action Detectionmentioning

confidence: 99%

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A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir,

Kim

2023

Electronics

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Smart cities are being developed worldwide with the use of technology to improve the quality of life of citizens and enhance their safety. Video surveillance is a key component of smart city infrastructure, as it involves the installation of cameras at strategic locations throughout the city for monitoring public spaces and providing real-time surveillance footage to law enforcement and other city representatives. Video surveillance systems have evolved rapidly in recent years, and are now integrated with advanced technologies like deep learning, blockchain, edge computing, and cloud computing. This study provides a comprehensive overview of video surveillance systems in smart cities, as well as the functions and challenges of those systems. The aim of this paper is to highlight the importance of video surveillance systems in smart cities and to provide insights into how they could be used to enhance safety, security, and the overall quality of life for citizens.

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Section: Motion-based Object Detectionmentioning

confidence: 99%

Section: Abnormal Action Detectionmentioning

confidence: 99%

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir,

Kim

2023

Electronics

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“…Video image salient target detection is to simulate human visual perception system, intelligently detect salient targets in video images from semantic level, and finally realize independent analysis and understanding of video image content [5][6][7][8][9][10][11]. Traditional target detection of video images is often used to distinguish the relevant classification of large categories of targets, in the case of complex and diverse image content, it can not capture enough visual cues, which makes it difficult to distinguish small differences between categories [12][13][14][15][16][17][18][19][20][21][22]. To solve this problem, it's impossible to rely on all kinds of artificial image annotation to prompt which areas the detection model needs to extract which target feature information.…”

Section: Introductionmentioning

confidence: 99%

Salient Target Detection Method of Video Images Based on Convolution Neural Network

Yao¹,

Guo²

2022

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Traditional target detection of video images is often used to distinguish the relevant classification of large categories of targets, in the case of complex and diverse image content, it cannot capture enough visual cues, which makes it difficult to distinguish small differences between categories. Therefore, this article studies the salient target detection method of video images based on convolution neural network. Based on dictionary learning, the dynamic features of videos are extracted, and then the coefficient matrix is generated based on the dictionary to complete the learning, so as to realize the complete description of the underlying dynamics of videos. DMD algorithm is used to extract the dynamic mode of videos, and finally the foreground and background of video image frames are separated. Based on YOLOv4 network model, the salient target detection model of video images is constructed. Aiming at the defects of YOLOv4 network model, such as redundant parameters, many convolution modules and complex architecture, a series of model optimization are carried out. Experimental results verify the effectiveness of the model.

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“…This operation is known as the "optical flow". The optical flow is considered the most effective method to generate good results for motion estimation [4]. There are two major methods that highlight the computation of the optical flow to solve the motion estimation problem: matching techniques and differential methods [5,6].…”

Section: Introductionmentioning

confidence: 99%

Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System

2021

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Motion estimation has become one of the most important techniques used in realtime computer vision application. There are several algorithms to estimate object motions. One of the most widespread techniques consists of calculating the apparent velocity field observed between two successive images of the same scene, known as the optical flow. However, the high accuracy of dense optical flow estimation is costly in run time. In this context, we designed an accurate motion estimation system based on the calculation of the optical flow of a moving object using the Lucas–Kanade algorithm. Our approach was applied on a local treatment region implemented into Raspberry Pi 4, with several improvements. The efficiency of our accurate realtime implementation was demonstrated by the experimental results, showing better performance than with the conventional calculation.

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RETRACTED: Adaptive motion estimation and sequential outline separation based moving object detection in video surveillance system

Cited by 24 publications

References 8 publications

A Survey of Video Surveillance Systems in Smart City

A Survey of Video Surveillance Systems in Smart City

Salient Target Detection Method of Video Images Based on Convolution Neural Network

Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System

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