Big data analytics of crime prevention and control based on image processing upon cloud computing

Xu, Zheng; Cheng, Cheng; Sugumaran, Vijayan

doi:10.20517/jsss.2020.04

Cited by 18 publications

(11 citation statements)

References 17 publications

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“…Videos contain more time domain information than pictures, so the video-based detection and extraction model has higher requirements for detection speed; at the same time, the instability of the video shooting scene will lead to camera shaking, motion blur, and so on, which increase the difficulty of video recognition. Nowadays, MHV [ 17 ], hidden Markov model [ 18 ], and so on are commonly used in video-based detection and extraction, but the detection effect is generally poor. In order to improve the detection speed and extraction accuracy of the model, this paper introduces the PSO-enabled LSTM neural network and takes the dance video as the detection carrier to realize the detection and extraction of human skeleton based on videos [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Human Skeleton Detection and Extraction in Dance Video Based on PSO‐Enabled LSTM Neural Network

2021

Computational Intelligence and Neuroscience

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With the significant increase of social informatization, the emerging information technology represented by machine vision has been applied to more and more scenes. Among them, the detection and extraction of human skeleton in a dance video based on this technology has a huge market demand in education and training. However, the existing detection and extraction technology has the problems of slow recognition speed and low extraction accuracy. Therefore, this paper proposes a neural network based on particle swarm optimization to detect and extract human skeletons in a dance video. Through the research and test on different data sets, it is found that the neural network based on particle swarm optimization algorithm has good detection and extraction ability and has high accuracy for the detection and recognition of human skeleton points. Among them, on all MPII data sets, the average accuracy of PSO-LSTM proposed in this paper is 3.9% higher than that of other optimal algorithms; on the PoseTrack data set, the average accuracy of detection and extraction is improved by 2.3%. The above results show that the neural network based on particle swarm optimization has fast detection speed and good extraction accuracy and can be used for the detection and extraction of human skeleton in a dance video.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Human Skeleton Detection and Extraction in Dance Video Based on PSO‐Enabled LSTM Neural Network

2021

Computational Intelligence and Neuroscience

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“…This method is more suitable for use in urban scenic spots, and its early warning focuses on the tourism safety problems caused by human factors. According to the characteristics of natural scenic spots, some scholars proposed to establish the risk identification and evaluation model of natural scenic spots through the combination of the GIS and Bayesian network model [ 11 ]. This method has strong pertinence and can clarify the scope of risk and improve the accuracy of tourism safety early warning, but it needs long-term effective data as the basis of decision-making, which greatly increases the time cost.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Optimization Algorithm of Tourism Security Early Warning Information System Based on Long Short‐Term Memory (LSTM)

Feng

2021

Computational Intelligence and Neuroscience

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Tourism safety is the focus of the tourism industry. It is not only related to the safety of tourists’ lives and property, but also related to social stability and sustainable development of the tourism industry. However, the security early warning of many scenic spots focuses on the response measures and remedial plans after the occurrence of security incidents, and the staff of many scenic spots have limited security awareness and information analysis ability, which is prone to lag in information release, and do not pay attention to the information of potential security problems. Therefore, this paper studies the optimization algorithm of the tourism security early warning information system based on the LSTM model and uses the recurrent neural network and LSTM to improve the processing and prediction ability of time-series data. The experimental results show that the number of three hidden layers in the tourism security early warning information system based on the LSTM model can reduce the training time of the model and improve the performance. Compared with the tourism safety early warning information system based on the BP neural network, it has better accuracy and stability, has better processing and prediction ability for time series data, and can monitor and analyze data scientifically in real-time and dynamically analyze data.

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“…In addition, if the moving target does not move at a constant speed during the movement, it may move at a variable speed. Therefore, using the frame difference method may result in the detection of moving targets or only relatively small and shallow boundaries [16]. However, although the karate diff method cannot accurately derive the moving target, this method is usually used as the original algorithm to quickly determine whether the target enters the scene [17].…”

Section: Frame Differencementioning

confidence: 99%

Basketball shooting technology based on acceleration sensor fusion motion capture technology

Zhao

Liu

2021

EURASIP J. Adv. Signal Process.

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Computer vision recognition refers to the use of cameras and computers to replace the human eyes with computer vision, such as target recognition, tracking, measurement, and in-depth graphics processing, to process images to make them more suitable for human vision. Aiming at the problem of combining basketball shooting technology with visual recognition motion capture technology, this article mainly introduces the research of basketball shooting technology based on computer vision recognition fusion motion capture technology. This paper proposes that this technology first performs preprocessing operations such as background removal and filtering denoising on the acquired shooting video images to obtain the action characteristics of the characters in the video sequence and then uses the support vector machine (SVM) and the Gaussian mixture model to obtain the characteristics of the objects. Part of the data samples are extracted from the sample set for the learning and training of the model. After the training is completed, the other parts are classified and recognized. The simulation test results of the action database and the real shot video show that the support vector machine (SVM) can more quickly and effectively identify the actions that appear in the shot video, and the average recognition accuracy rate reaches 95.9%, which verifies the application and feasibility of this technology in the recognition of shooting actions is conducive to follow up and improve shooting techniques.

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Big data analytics of crime prevention and control based on image processing upon cloud computing

Cited by 18 publications

References 17 publications

[Retracted] Human Skeleton Detection and Extraction in Dance Video Based on PSO‐Enabled LSTM Neural Network

[Retracted] Human Skeleton Detection and Extraction in Dance Video Based on PSO‐Enabled LSTM Neural Network

[Retracted] Optimization Algorithm of Tourism Security Early Warning Information System Based on Long Short‐Term Memory (LSTM)

Basketball shooting technology based on acceleration sensor fusion motion capture technology

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