Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chien‐Hsiung

doi:10.1117/1.jei.26.5.051402

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…Some technologies have been successfully applied in the real world. The literature must clearly recognize that the general human body detection has a variety of postures, the color and texture of the clothes are widely scattered inside, the background caused by camera movement changes, there is mutual shielding among pedestrians in crowded environments [ 14 ], etc. Detection has received extensive attention in the field of computer vision.…”

Section: Related Workmentioning

confidence: 99%

Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System

Wang

2022

Computational Intelligence and Neuroscience

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Pedestrian detection and tracking based on computer vision has gradually become an international pattern recognition, which is one of the most active research topics in the field of computer vision and artificial intelligence. Using the theoretical results in the field of pattern recognition and computer vision technology, we are committed to detect and track pedestrians from video sequences. In addition to computer vision-based passer-by detection and tracking technology as the key, in the advanced computer vision action and analysis, it has a direct impact on the accuracy and robustness of its understanding. We analyzed various targets, such as subsequent recognition motion and pedestrian motion, and described them as high-level application processing, such as action understanding. In addition, because of the unique texture of human clothes compared with the surrounding natural landscape, they are highly “prominent” from the perspective of human visual system, and they are particularly prominent in the peripheral part of human contact with the background. In this paper, a binary function based on importance is proposed. As the space representation of image itself is not sensitive to noise and local signal, space representation is used. In addition, as an observation model, it can reduce the adverse effects of background noise and local noise on the tracking algorithm. Through the function block tracking, the pedestrian’s body can be tracked in detail. At the same time, the color band learning method is used to update the target template online to deal with the changes of target appearance caused by sunshine, pedestrian posture, and other factors. According to the experimental results, even if the appearance and posture of pedestrians change greatly, it has a stable tracking effect.

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Section: Related Workmentioning

confidence: 99%

Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System

Wang

2022

Computational Intelligence and Neuroscience

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“…But after all, the intelligent transportation system is a discipline created by the convergence of electronic information technology, Internet of ings technology, automatic control theory, communication technology, and traditional traffic engineering theory, which means that its establishment must rely on a wealth of theoretical foundations [19,20]. en, a lot of infrastructure construction is added to form a large-scale and comprehensive transportation system, so that it can give full play to its role [21].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Video Surveillance Technology in Intelligent Transportation

2020

Journal of Advanced Transportation

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Along with the strength of the country’s overall strength, the people’s pockets have become more and more popular, and there have been significant improvements in all aspects of life, especially in terms of travel methods. This reflects the increase in residents’ income, but it also brings huge traffic pressure. In the long run, traffic congestion is not only detrimental to urban development, but frequent traffic accidents threaten residents’ travel safety. Effective monitoring methods are essential to solving these problems, so it is necessary to carry out research on intelligent video monitoring technology in intelligent transportation. The purpose of this article is to solve the current situation of excessive traffic pressure in the city. Through the study of intelligent video surveillance technology in intelligent traffic, the use of constrained least squares algorithm to remove motion blur and apply Kalan filtering to the sharpening process is used to eliminate noise ambiguity and make a brief introduction to various classic moving target detection methods to realize real-time monitoring of intelligent traffic conditions and continuously adjust and verify the monitoring situation, and then establish intelligent video in intelligent traffic monitoring technology research system. The research results show that this kind of intelligent video surveillance technology research in intelligent transportation can effectively increase the awareness of intelligent video surveillance technology and improve the level of intelligent video surveillance technology. The data measurement time has been shortened by one hour, the aggregation time has been changed from three hours to two hours, and the analysis time has been shortened by half. Eased urban traffic road pressure and greatly reduced the incidence of traffic accidents, which is conducive to socialist harmony social construction.

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“…The main differences between our framework and the other existing frameworks are as follows: a) Drawing out highly descriptive features, our framework utilizes two efficient motion algorithms while maintaining online-performance. In particular, it utilizes optical flow and background subtraction features, both of which effectively and independently utilized in the past [10,23]. b) The proposed framework is different from [24] in three aspects: the first one is that the proposed framework utilizes from extracting foreground features besides the ones from optical flow.…”

Section: Introductionmentioning

confidence: 99%

Online video-based abnormal detection using highly motion techniques and statistical measures

Al-Dhamari¹,

Sudirman²,

Mahmood³

et al. 2019

TELKOMNIKA

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At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between frame processing time and detection accuracy in abnormal detection approaches. Therefore, the primary challenge is to balance this trade-off suitably by utilizing few, but very descriptive features to fulfill online performance while maintaining a high accuracy rate. In this study, we propose a new framework, which achieves the balancing between detection accuracy and video processing time by employing two efficient motion techniques, specifically, foreground and optical flow energy. Moreover, we use different statistical analysis measures of motion features to get robust inference method to distinguish abnormal behavior incident from normal ones. The performance of this framework has been extensively evaluated in terms of the detection accuracy, the area under the curve (AUC) and frame processing time. Simulation results and comparisons with ten relevant online and non-online frameworks demonstrate that our framework efficiently achieves superior performance to those frameworks, in which it presents high values for the accuracy while attaining simultaneously low values for the processing time.

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Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

Cited by 7 publications

References 37 publications

Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System

Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System

Intelligent Video Surveillance Technology in Intelligent Transportation

Online video-based abnormal detection using highly motion techniques and statistical measures

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