Citation

Tay, Nian Chi and Tee, Connie and Ong, Thian Song and Goh, Kah Ong Michael and Teh, Pin Shen (2019) A Robust Abnormal Behavior Detection Method Using Convolutional Neural Network. In: Computational Science and Technology. Springer, Lecture Notes in Electrical Engineering, pp. 37-47. ISBN 978-981-13-2622-6

Text 42.pdf - Published Version Restricted to Repository staff only Download (924kB)

Abstract

A behavior is considered abnormal when it is seen as unusual under certain contexts. The definition for abnormal behavior varies depending on situations. For example, people running in a field is considered normal but is deemed abnormal if it takes place in a mall. Similarly, loitering in the alleys, fighting or pushing each other in public areas are considered abnormal under specific circumstances. Abnormal behavior detection is crucial due to the increasing crime rate in the society. If an abnormal behavior can be detected earlier, tragedies can be avoided. In recent years, deep learning has been widely applied in the computer vision field and has acquired great success for human detection. In particular, Convolutional Neural Network (CNN) has shown to have achieved state-of-the-art performance in human detection. In this paper, a CNN-based abnormal behavior detection method is presented. The proposed approach automatically learns the most discriminative characteristics pertaining to human behavior from a large pool of videos containing normal and abnormal behaviors. Since the interpretation for abnormal behavior varies across contexts, extensive experiments have been carried out to assess various conditions and scopes including crowd and single person behavior detection and recognition. The proposed method represents an end-to-end solution to deal with abnormal behavior under different conditions including variations in background, number of subjects (individual, two persons or crowd), and a range of diverse unusual human activities. Experiments on five benchmark datasets validate the performance of the proposed approach.

Item Type:	Book Section
Uncontrolled Keywords:	Convolutional Neural Network, deep learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	26 Jan 2022 02:42
Last Modified:	26 Jan 2022 02:42
URII:	http://shdl.mmu.edu.my/id/eprint/9010

Downloads

Edit (login required)