Monash University
Monash_Thesis_PhD_LimJunYi_Revised.pdf (47.67 MB)

Real-time Aggressive Action Recognition using Deep Learning in a Multifarious Setting

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posted on 2023-02-12, 14:56 authored by JUN YI LIM
The epidemic of violent crimes worldwide necessitates an active-based video surveillance network to combat these criminal acts. In this context, autonomously detecting aggressors and weapons is crucial in modelling human-weapon interactions for aggressive action recognition. However, current object detectors and human-object interaction detectors using deep learning cannot reliably capture surveillance-based humans and weapons in multifarious and complex scenarios. To address these problems, this research puts forward a novel surveillance-based human-object interaction (HOI) detector with an aggressive HOI dataset for accurate aggressive action detection. In the end, the outcomes of this research could potentially pioneer real-time crime detection in video surveillance.


Principal supervisor

Joanne Lim

Year of Award


Department, School or Centre

School of Engineering (Monash University Malaysia)


Doctor of Philosophy

Degree Type



Faculty of Engineering