A Transformer-Based Model for Abnormal Activity Recognition
Given the increasing daily volume of videos generated by security cameras in personal and public spaces, monitoring the activities present in videos has become crucial. Many video surveillance systems are designed to verify performance accuracy and provide alerts during the occurrence of abnormal ac...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
Semnan University
2024-04-01
|
| Series: | مجله مدل سازی در مهندسی |
| Subjects: | |
| Online Access: | https://modelling.semnan.ac.ir/article_8569_1733b9593b801778c3d2c53dbebcc0b4.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Given the increasing daily volume of videos generated by security cameras in personal and public spaces, monitoring the activities present in videos has become crucial. Many video surveillance systems are designed to verify performance accuracy and provide alerts during the occurrence of abnormal activities. In this regard, various intelligent models have been proposed for detecting activities in videos. Considering recent advances in artificial intelligence, particularly deep learning, this paper introduces a model based on the Transformer network. To reduce computational complexity, keypoints of the human body are utilized in this approach. Fifteen key body points are input into the Transformer model, leveraging parallel processing during training and a self-attention mechanism. This enhances the speed and accuracy of the model. Experimental results on the JHMDB public database indicate an improvement in the accuracy of detecting abnormal activities compared to baseline models. |
|---|---|
| ISSN: | 2008-4854 2783-2538 |