A Transformer-Based Model for Abnormal Activity Recognition

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Ahmadi Amir Mohammad; Kiani Kourosh; Rastgoo Razieh

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Journal Paper

Paper Information

Journal: Modeling in Engineering Year:2024 | Volume:22 | Issue:76 Page(s): 213-221

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Information Journal Paper

Title

A Transformer-Based Model for Abnormal Activity Recognition

Author(s)

Ahmadi Amir Mohammad | Kiani Kourosh | Rastgoo Razieh | Issue Writer Certificate

Keywords

Video processing

Video surveillance

Abnormal activities

Deep learning

Transformer Network

Abstract

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.

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