Open Access

ARTICLE

Automatic Unusual Activities Recognition Using Deep Learning in Academia

by Muhammad Ramzan^1,2,*, Adnan Abid¹, Shahid Mahmood Awan^1,3

1 School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan.
2 Department of Computer Science and Information Technology, University of Sargodha, Sargodha, 40100, Pakistan
3 School of Electronics, Computing and Mathematics, University of Derby, Derby, United Kingdom

* Corresponding Author: Muhammad Ramzan. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)

Computers, Materials & Continua 2022, 70(1), 1829-1844. https://doi.org/10.32604/cmc.2022.017522

Received 02 February 2021; Accepted 25 April 2021; Issue published 07 September 2021

Abstract

In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or using unfair means. To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities. The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse configurations. The evaluation using standard performance measures confirm that the AUAR model outperforms the already proposed approaches for unusual activity recognition. Apart from evaluating the proposed model on the examination dataset, we also apply AUAR on Violent and Movies datasets, widely used in the relevant literature to detect suspicious activities. The results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.

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