Combating street crimes using AI
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Marcus Lim Jun Yi
Snatch thefts and robberies seem like relatively common occurrences, but they can be fatal at times. Statistics show that over 2,000 snatch thefts or robberies were reported in Malaysia in 2019 alone, and this number could be substantially higher when factoring in unreported cases.
To curb these crimes, city councillors, retailers, commercial and residential management have ramped up the deployment of closed-circuit television (CCTV) cameras across urban areas.
These trends mentioned above, however, are at odds with each other. On the one hand, street robberies are projected to rise as the country continues with its rapid urbanisation in a post-pandemic era.
On the other hand, video surveillance is a quintessentially passive driven system, whereby recorded or archived content is used primarily as evidence of a criminal activity that has taken place. More often, these perpetrators can evade apprehension due to the delayed response of alerting authorities. Given this conflict, a natural question to ask is: Can a citizen remain safe despite the presence of CCTV cameras?
To answer this question, researchers at the School of Information Technology, Monash University Malaysia, embarked on a research and development endeavour to transform conventional CCTVs into an autonomously intelligent system to detect street crimes in real-time.
This endeavour is led by Dr Vishnu Monn Baskaran and PhD student Marcus Lim Jun Yi and funded by the Ministry of Higher Education’s Fundamental Research Grant Scheme. The feasibility of this research is motivated by the rapid evolution of artificial intelligence and deep neural network algorithms.
Vishnu Monn
There is a new opportunity to realise a reliable smart video surveillance framework, coupled with significant advancements in high-performance computing technology. Typically, there are three stages in a smart video surveillance platform.
The first stage involves having AI-based software to process live video surveillance images to detect weapons. In most cases, urban robberies would involve the usage of weapons such as guns. Automatically identifying the presence of a weapon from a surveillance camera in real-time would increase the software’s reliability in assessing a threat within a surveilled area.
The second stage involves formulating a relation between the human wielding the weapon and the weapon itself for aggressive action recognition. Most importantly, the first and second stages are executed autonomously using AI developed software with minimal manual intervention. The third stage generates an alert that is relayed to medical crews and law enforcement officers to dispatch them quickly to provide aid to the victim and apprehend the perpetrator.
The significance of a real-time alert and response mechanism could re-envision how AI is used to strengthen law enforcement and to further deter criminal activities in enhancing public safety.
Presently, the research team at Monash University Malaysia has completed stage one in developing a smart surveillance system that can detect handguns from surveillance cameras in real-time accurately. The team initially focused on automated handgun detection, given that crimes using firearms are more prevalent globally, especially in the north and south of America and in parts of southeast Asia.
The outcomes of their research were published in the Engineering Applications of Artificial Intelligence journal. The team also won a gold medal for their project, Monash Automatic Gun Detection System (MAGTS), at the 31st International Invention, Innovation & Technology Exhibition 2020 (ITEX 2020).
Dr Vishnu Monn, Marcus Lim and the team are now focusing their efforts towards formulating an accurate human to weapon relation model for classifying aggressive human actions, which represents the second stage in realising a smart video surveillance platform. They are also fine-tuning the outcomes from stage one of their research to detect knives and machetes, which are more prevalent in robberies that are carried out in Malaysia.
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