Abnormal behavior recognition for intelligent video surveillance systems: A review

Mabrouk, Amira Ben; Zagrouba, Ezzeddine

doi:10.1016/j.eswa.2017.09.029

Cited by 292 publications

(125 citation statements)

References 61 publications

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“…Ongoing digitalization of railways open opportunities for new and expanding measurements of ridership. As an example of the possibilities following digitalization and technological development, Mabrouk and Zagrouba (2018) review how video surveillance system can be used to map behaviour, including travellers on train stations. In the following, we briefly review some of the background for Big Data in transportation and implications for ridership measurements, including quality assessments.…”

Section: Analysing Ridership On Trains Quality Assessments and Big Dmentioning

confidence: 99%

Approaches, technologies and importance of analysis of the number of train travellers

Sørensen

Olsson

Akhtar

et al. 2019

Urban, Planning and Transport Research

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Section: Analysing Ridership On Trains Quality Assessments and Big Dmentioning

confidence: 99%

Approaches, technologies and importance of analysis of the number of train travellers

Sørensen

Olsson

Akhtar

et al. 2019

Urban, Planning and Transport Research

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“…Video surveillance systems have been adopted mainly for security and safety purposes. Especially, abnormal behavior detection is receiving attention in research [13]. For example [14], developed a monitoring system for the elderly in home environments focusing on fall detection.…”

Section: Video Surveillancementioning

confidence: 99%

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

et al. 2018

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“…Among the latter there are methods focusing on fights [72], methods specialized on violence [73,74,75,76], on aggressive behaviors [77], and on crime [78]. A review on methods for detecting abnormal behaviors, taking into account some of the above mentioned ones, and also discussing available datasets, is provided in [79].…”

Section: Related Work and Datasets On Abnormal Behaviorsmentioning

confidence: 99%

Discovery and recognition of motion primitives in human activities

2019

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We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the ‘motion flux’, a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis.

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Abnormal behavior recognition for intelligent video surveillance systems: A review

Cited by 292 publications

References 61 publications

Approaches, technologies and importance of analysis of the number of train travellers

Approaches, technologies and importance of analysis of the number of train travellers

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

Discovery and recognition of motion primitives in human activities

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