A review on classifying abnormal behavior in crowd scene

Afiq, A. A.; Zakariya, M. A.; Saad, Mohamad Naufal Mohamad; Nurfarzana, A. A.; Khir, M. H. Md; Fadzil, Ahmad Firdaus Ahmad; Jale, A.; Wang, Gunawan; Izuddin, Z. A. A.; Faizari, M.

doi:10.1016/j.jvcir.2018.11.035

Cited by 47 publications

(39 citation statements)

References 130 publications

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“…For instance, understanding crowd behavior is deemed essential to surveillance and security purposes. Motivated by this fact, many surveys are devoted to crowd analysis such as [1,23,59,186], in order to provide an overview of the major techniques applicable for classifying abnormal behavior in a crowded scene scenario. On the other hand, reviews on vision-based Ambient Assisted Living, patient monitoring like [123,127,139], fall detection and abnormal human activity recognition such as [18,39] were proposed in the literature.…”

Section: Related Surveysmentioning

confidence: 99%

Vision-based human activity recognition: a survey

Beddiar

Nini

Sabokrou

et al. 2020

Multimed Tools Appl

327

117

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Human activity recognition (HAR) systems attempt to automatically identify and analyze human activities using acquired information from various types of sensors. Although several extensive review papers have already been published in the general HAR topics, the growing technologies in the field as well as the multi-disciplinary nature of HAR prompt the need for constant updates in the field. In this respect, this paper attempts to review and summarize the progress of HAR systems from the computer vision perspective. Indeed, most computer vision applications such as human computer interaction, virtual reality, security, video surveillance and home monitoring are highly correlated to HAR tasks. This establishes new trend and milestone in the development cycle of HAR systems. Therefore, the current survey aims to provide the reader with an up to date analysis of vision-based HAR related literature and recent progress in the field. At the same time, it will highlight the main challenges and future directions.

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Section: Related Surveysmentioning

confidence: 99%

Vision-based human activity recognition: a survey

Beddiar

Nini

Sabokrou

et al. 2020

Multimed Tools Appl

327

117

View full text Add to dashboard Cite

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“…A review on classifying abnormal behavior in crowd scene [77] GMM and crowd context modelling is done through MRF. GPMM adds one extra feture such as count of occurrence of observed behaviour.…”

Section: Review In the Field Of Crowd Analysismentioning

confidence: 99%

“…Application areas where surveillance cameras are unavoidable are discussed. Current status and trends in video analysis are revealed through A review on classifying abnormal behavior in crowd scene [77] 2019 ScienceDirect…”

Section: Introductionmentioning

confidence: 99%

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

2019

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“…But the long-time static target is difficult to be detected out accurately. Zhang et al (2018Zhang et al ( , 2019, Afiq et al (2019) applied dynamic target detection to crowd action and emotion recognition. In addition, the lack of laser radar can bring instable judgment for some obstacles with special material (such as glass doors), which can affect the accuracy of the map (Einhorn and Gross, 2015), achieved estimation tracking of the dynamic object by combining the normal distribution transformation with the grid map.…”

Section: Related Workmentioning

confidence: 99%

Walking Human Detection Using Stereo Camera Based on Feature Classification Algorithm of Second Re-projection Error

et al. 2019

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This paper presents a feature classification method based on vision sensor in dynamic environment. Aiming at the detected targets, a double-projection error based on orb and surf is proposed, which combines texture constraints and region constraints to achieve accurate feature classification in four different environments. For dynamic targets with different velocities, the proposed classification framework can effectively reduce the impact of large-area moving targets. The algorithm can classify static and dynamic feature objects and optimize the conversion relationship between frames only through visual sensors. The experimental results show that the proposed algorithm is superior to other algorithms in both static and dynamic environments.

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A review on classifying abnormal behavior in crowd scene

Cited by 47 publications

References 130 publications

Vision-based human activity recognition: a survey

Vision-based human activity recognition: a survey

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

Walking Human Detection Using Stereo Camera Based on Feature Classification Algorithm of Second Re-projection Error

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