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2022 | OriginalPaper | Buchkapitel

A Systematic Analysis of the Human Activity Recognition Systems for Video Surveillance

verfasst von : Sonika Jindal, Monika Sachdeva, Alok Kumar Singh Kushwaha

Erschienen in: IoT and Analytics for Sensor Networks

Verlag: Springer Singapore

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Abstract

In recent years, human activity recognition has become a prominent research area in numerous fields such as healthcare, smart home activity analysis, suspicious activity recognition, robotics, surveillance, and security. The focus of the current research work is the analysis of human activity recognition systems for video surveillance. The human activity recognition system involves the detection of normal as well as abnormal activities. The recognition of human activities is still considered a challenging issue despite the contributions of numerous researchers. The erratic human behavior and complexities of the video datasets create numerous challenges to precisely observe the human activities with significant performance. The analysis of the human activity detection systems for video surveillance is conducted on the basis of state-of-art contributions by different researchers in the field. The paper also describes the taxonomy of human activity detection. It ends with a discussion of the challenging issues in the field along with the concluding remarks.

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Metadaten
Titel
A Systematic Analysis of the Human Activity Recognition Systems for Video Surveillance
verfasst von
Sonika Jindal
Monika Sachdeva
Alok Kumar Singh Kushwaha
Copyright-Jahr
2022
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-16-2919-8_31

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