Islam, Md Shamimul and Hasan, Md Mahedi and Abdullah, Sohaib and Md Akbar, Jalal Uddin and Arafat, N. H.M. and Murad, Saydul Akbar (2021) A deep Spatio-temporal network for vision-based sexual harassment detection. In: 2021 Emerging Technology in Computing, Communication and Electronics, ETCCE 2021. 2021 Emerging Technology in Computing, Communication and Electronics, ETCCE 2021 , 21 - 23 December 2021 , Dhaka. pp. 1-6.. ISBN 978-166548364-3 (Published)
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Abstract
Smart surveillance systems can play a significant role in detecting sexual harassment in real-time for law enforcement which can reduce the sexual harassment activities. Real-time detecting of sexual harassment from video is a complex computer vision because of various factors such as clothing or carrying variation, illumination variation, partial occlusion, low resolution, view angle variation etc. Due to the advancement of convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM), human action recognition tasks have achieved great success in recent years. But sexual harassment detection is addressed due to presences of large-scale harassment dataset. In this work, to address this problem, we build a video dataset of sexual harassment, namely Sexual harassment video (SHV) dataset which consists of harassment and non-harassment videos collected from YouTube. Besides, we build a CNN-LSTM network to detect the sexual harassment in which CNN and RNN are employed for extracting spatial features and temporal features, respectively. State-of-the-art pretrained models are also employed as a spatial feature extractor with an LSTM and three dense layer to classify harassment activities. Moreover, to find the robustness of our proposed model, we have conducted several experiments with our proposed method on two other benchmark datasets, such as Hockey Fight dataset and Movie Violence dataset and achieved state-of-the-art accuracy.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Deep learning; Sexual harassment; Surveillance systems |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 30 Oct 2024 04:38 |
| Last Modified: | 30 Oct 2024 04:38 |
| URI: | http://umpir.ump.edu.my/id/eprint/42383 |
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