Automated detection of fighting styles using localized action features

Mishra, Ankush Aniket; Srinivasa, Gowri

doi:10.1109/icisc.2018.8399036

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…In the second phase, the activity is recognized based on the extracted features [18]. The fight action recognition framework proposed in [19] used a bag of words for feature extraction and K-nearest neighbors for classification. The model achieved an accuracy of 86% using the k-th video dataset.…”

Section: Trajectory-based Methods For Violence Detectionmentioning

confidence: 99%

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Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

Ahmed¹,

Ramzan²,

Khan³

et al. 2021

Computers, Materials &Amp; Continua

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Violence recognition is crucial because of its applications in activities related to security and law enforcement. Existing semi-automated systems have issues such as tedious manual surveillances, which causes human errors and makes these systems less effective. Several approaches have been proposed using trajectory-based, non-object-centric, and deep-learning-based methods. Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods. However, the their performance must be improved. This study explores the state-of-the-art deep learning architecture of convolutional neural networks (CNNs) and inception V4 to detect and recognize violence using video data. In the proposed framework, the keyframe extraction technique eliminates duplicate consecutive frames. This keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate frames. For feature selection and classification tasks, the applied sequential CNN uses one kernel size, whereas the inception v4 CNN uses multiple kernels for different layers of the architecture. For empirical analysis, four widely used standard datasets are used with diverse activities. The results confirm that the proposed approach attains 98% accuracy, reduces the computational cost, and outperforms the existing techniques of violence detection and recognition.

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Section: Trajectory-based Methods For Violence Detectionmentioning

confidence: 99%

“…Feature extraction Classification Accuracy % [19] Begs of words (BoW) k-NN 86 [20] Gaussian mixture method (GMM) Rule-based 90 [21] Region Vector Motion (RVM) SVM 96 [18] Motion boundary histograms (MBH) SVM 89 [22] Local motion feature (LMF) SVM 85…”

Section: Referencementioning

confidence: 99%

Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

Ahmed¹,

Ramzan²,

Khan³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Traditional Methods Machine Learning [37] SVM [28,44,19,46] Others [49,46] Handcrafted Features [11,93] Motion Patterns and Optical Flow [11,29,15,47,10,97,23…”

Section: Violence Detection Literaturementioning

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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A Novel Optimization Algorithm for Smart Video Surveillance System and Change Object Detection

Siddiqui,

Siddiqi

2023

Communications in Computer and Information Science

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Automated detection of fighting styles using localized action features

Cited by 7 publications

References 13 publications

Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

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

A Novel Optimization Algorithm for Smart Video Surveillance System and Change Object Detection

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