Earticle

초록

영어

Nowadays, surveillance systems play a pivotal role in monitoring various sectors to ensure public safety and security. These systems generate massive amounts of video data. Therefore, effective analysis of these streams is an important research area with multiple applications. Several methods have been reported for the automatic recognition of abnormal activities, but these techniques show limited performance while learning complex temporal dependencies of real-world surveillance of abnormal activities. We introduce a Deep Learning (DL)-assisted framework that is mainly divided into two parts. First, the surveillance video stream is preprocessed, and then the BoTNeT-152 is employed to extract spatial features. Secondly, a Residual Deep Bidirectional Long Short-Term Memory (RBLSMT) Network is introduced to learn the complex temporal dependencies across multiple frames for abnormal activity recognition. To assess the effectiveness of our proposed method, we evaluated its performance on the benchmark real-world UCFCrime2Local dataset, achieving an accuracy of 86% reveals a significant improvement of up to 2% compared to existing methods which shows the superiority of the suggested technique in addressing the challenges posed by complex surveillance environments.

목차

Abstract
1. Introduction
2. Methods
2.1. Features Extraction and Sequential Learning
3. Experiment Result
3.1. Experiment Setup
3.2 Dataset
3.3 Experimental Results and Comparison
4. Conclusions
Acknowledgment
References

저자

• Altaf Hussain [ Sejong university ]
• Noman Khan [ Sejong university ]
• Muhammad Munsif [ Sejong university ]
• Adnan Hussain [ Sejong university ]
• Min Je Kim [ Sejong university ]
• Sang Il Yoon [ Sejong university ]
• Sung Wook Baik [ Sejong university ] Corresponding Author

참고문헌