CAE: Towards Crowd Anarchism Exploration

Towards effective violence detection in densely crowded scenes, we introduce two novel computationally inexpensive real time pipelines. One is for automatic crowd violence detection and another for capturing the region with highest crowd concentration. The proposed violence detection architecture us...

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Bibliographic Details
Published in2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) pp. 559 - 564
Main Authors Raha, Mayamin Hamid, Deb, Tonmoay, Rahmun, Mahieyin, Bijoy, Shahriar Ali, Firoze, Adnan, Khan, Mohammad Ashrafuzzaman
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2020
Subjects
Benchmark testing
Bounding Box
Computer architecture
Dense HMG
Dense HOG
Discrete cosine transforms
Feature extraction
Histograms
IoU
Pipelines
RBF SVM
Support vector machines
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Summary:Towards effective violence detection in densely crowded scenes, we introduce two novel computationally inexpensive real time pipelines. One is for automatic crowd violence detection and another for capturing the region with highest crowd concentration. The proposed violence detection architecture uses dense Histogram of Oriented Gradients (HOG) and dense Histogram of Motion Gradients (HMG) for feature extraction and Radial Bias Function Support Vector machine (RBF SVM) for classification. We further contribute by introducing a benchmark dataset, Dense Crowd Turbulence (DCT), having 120 videos of crowd violence and 120 for non-violence. DCT achieves an accuracy of 100% when evaluated with deep learning based violence detection frameworks. The violence detection architecture achieved a near the state of art accuracy of 87.3% on DCT.
DOI:10.1109/ICMLA51294.2020.00093