Research on Action Recognition and Content Analysis in videos based on DNN and MLN

Song, Wei; Yu, Jing; Zhao, Xibin; Wang, Antai

doi:10.32604/cmc.2019.06361

Cited by 10 publications

(5 citation statements)

References 40 publications

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“…Deep learning algorithms can accurately detect and track objects in video streams, even in complex and cluttered environments, and can detect and classify different types of objects, such as people, vehicles, and animals. Deep learning can be performed by various types of neural networks such as CNNs [39][40][41][42], DNNs [43,44], and RNNs [45,46], to recognize objects, track movements, and analyze behavior in video data [47]. To train on video data in a supervised learning task, it is necessary to pre-process and annotate the data.…”

Section: Cloud Computing and Deep Learningmentioning

confidence: 99%

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: Cloud Computing and Deep Learningmentioning

confidence: 99%

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir,

Kim

2023

Electronics

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“…Another promising technique for detecting intrusions into computer vision is 18,19 . Vision-based IDS uses computer vision technology to instantly detect intruders from the flow of media information and greatly improves the usability of surveillance applications.…”

Section: Related Workmentioning

confidence: 99%

Interior Visual Intruders Detection Module Based on Multi-Connect Architecture MCA Associative Memory

Kareem

2023

Baghdad Sci.J

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Most recent studies have focused on using modern intelligent techniques spatially, such as those developed in the Intruder Detection Module (IDS). Such techniques have been built based on modern artificial intelligence-based modules. Those modules act like a human brain. Thus, they should have had the ability to learn and recognize what they had learned. The importance of developing such systems came after the requests of customers and establishments to preserve their properties and avoid intruders' damage. This would be provided by an intelligent module that ensures the correct alarm. Thus, an interior visual intruder detection module depending on Multi-Connect Architecture Associative Memory (MCA) has been proposed. Via using the MCA associative memory as a new trend, the proposed module goes through two phases: the first is the training phase (which is executed once during the module installation process) and the second is the analysis phase. Both phases will be developed through the use of MCA, each according to its process.The training phase will take place through the learning phase of MCA, while the analysis phase will take place through the convergence phase of MCA. The use of MCA increases the efficiency of the training process for the proposed system by using a minimum number of training images that do not exceed 10 training images of the total number of frames in JPG format. The proposed module has been evaluated using 11,825 images that have been extracted from 11 tested videos. As a result, the module can detect the intruder with an accuracy ratio in the range of 97%-100%. The average training process time for the training videos was in the range of 10.2 s to 23.2 s.

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“…Content analysis of videos is also used in other fields. For example, Song et al [ 35 ] analyzed and reviewed the latest methods on the content analysis of the videos for action recognition. Instead of action recognition, the behaviors of humans are also used for the gesture, speech, and wrist-activity recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

A Smart Surveillance System for Uncooperative Gait Recognition Using Cycle Consistent Generative Adversarial Networks (CCGANs)

Alsaggaf

Mehmood

Khairullah

et al. 2021

Computational Intelligence and Neuroscience

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Surveillance remains an important research area, and it has many applications. Smart surveillance requires a high level of accuracy even when persons are uncooperative. Gait Recognition is the study of recognizing people by the way they walk even when they are unwilling to cooperate. It is another form of a behavioral biometric system in which unique attributes of an individual’s gait are analyzed to determine their identity. On the other hand, one of the big limitations of the gait recognition system is uncooperative environments in which both gallery and probe sets are made under different and unknown walking conditions. In order to tackle this problem, we propose a deep learning-based method that is trained on individuals with the normal walking condition, and to deal with an uncooperative environment and recognize the individual with any dynamic walking conditions, a cycle consistent generative adversarial network is used. This method translates a GEI disturbed from different covariate factors to a normal GEI. It works like unsupervised learning, and during its training, a GEI disrupts from different covariate factors of each individual and acts as a source domain while the normal walking conditions of individuals are our target domain to which translation is required. The cycle consistent GANs automatically find an individual pair with the help of the Cycle Loss function and generate the required GEI, which is tested by the CNN model to predict the person ID. The proposed system is evaluated over a publicly available data set named CASIA-B, and it achieved excellent results. Moreover, this system can be implemented in sensitive areas, like banks, seminar halls (events), airports, embassies, shopping malls, police stations, military areas, and other public service areas for security purposes.

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Research on Action Recognition and Content Analysis in videos based on DNN and MLN

Cited by 10 publications

References 40 publications

A Survey of Video Surveillance Systems in Smart City

A Survey of Video Surveillance Systems in Smart City

Interior Visual Intruders Detection Module Based on Multi-Connect Architecture MCA Associative Memory

A Smart Surveillance System for Uncooperative Gait Recognition Using Cycle Consistent Generative Adversarial Networks (CCGANs)

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