Automated Violence Detection in Video Crowd Using Spider Monkey-Grasshopper Optimization Oriented Optimal Feature Selection and Deep Neural Network

Naik, Anuja Jana; Gopalakrishna, M. T.

doi:10.1007/s40313-021-00868-w

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…Heading onward towards more sophisticated and reliable anomaly and violence detection methods [47], Mahadevan et al introduced a novel framework for anomaly detection in crowded scenes [53], that is a very challenging task to achieve. Their research concludes that a set of three properties acts as the main constituent of any anomaly detection system.…”

Section: Machine Learning Techniques For Vdmentioning

confidence: 99%

An overview of violence detection techniques: current challenges and future directions

et al. 2022

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The Big Video Data generated in today's smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis a difficult task in terms of computation and preciseness. Violence Detection (VD), broadly plunging under Action and Activity recognition domain, is used to analyze Big Video data for anomalous actions incurred due to humans. The VD literature is traditionally based on manually engineered features, though advancements to deep learning based standalone models are developed for

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Section: Machine Learning Techniques For Vdmentioning

confidence: 99%

An overview of violence detection techniques: current challenges and future directions

et al. 2022

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A spatio-temporal model for violence detection based on spatial and temporal attention modules and 2D CNNs

Mahmoodi,

Nezamabadi-pour

2024

Pattern Anal Applic

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A Comprehensive Survey on Feature Selection with Grasshopper Optimization Algorithm

Alirezapour,

Mansouri,

Mohammad Hasani Zade

2024

Neural Process Lett

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Recent growth in data dimensions presents challenges to data mining and machine learning. A high-dimensional dataset consists of several features. Data may include irrelevant or additional features. By removing these redundant and unwanted features, the dimensions of the data can be reduced. The feature selection process eliminates a small set of relevant and important features from a large data set, reducing the size of the dataset. Multiple optimization problems can be solved using metaheuristic algorithms. Recently, the Grasshopper Optimization Algorithm (GOA) has attracted the attention of researchers as a swarm intelligence algorithm based on metaheuristics. An extensive review of papers on GOA-based feature selection algorithms in the years 2018–2023 is presented based on extensive research in the area of feature selection and GOA. A comparison of GOA-based feature selection methods is presented, along with evaluation strategies and simulation environments in this paper. Furthermore, this study summarizes and classifies GOA in several areas. Although many researchers have introduced their novelty in the feature selection problem, many open challenges and enhancements remain. The survey concludes with a discussion about some open research challenges and problems that require further attention.

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Automated Violence Detection in Video Crowd Using Spider Monkey-Grasshopper Optimization Oriented Optimal Feature Selection and Deep Neural Network

Cited by 6 publications

References 39 publications

An overview of violence detection techniques: current challenges and future directions

An overview of violence detection techniques: current challenges and future directions

A spatio-temporal model for violence detection based on spatial and temporal attention modules and 2D CNNs

A Comprehensive Survey on Feature Selection with Grasshopper Optimization Algorithm

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