Published: Aug 13, 2021
computer vision trends and real-world applications
Computer vision presents an ability to allow computers to analyse large amounts of data - relating to how humans view and perceive their environment. A subfield of artificial intelligence, it has brought success to solving complex problems in various applications – allowing extraction of useful information contained in video images. In our current era, this field of study has found practicality in various industries, in particular, those that look toward public safety – with a focus on human activity and behaviour analysis.
Why it matters
With crucial applications in social, commercial, and educational domains, computer vision provides simple gesture recognition to complicated behavioural movements – leading to major development in techniques related to human motion representation and recognition. Further, these developments allow specific customisation in areas of urgency, such as the COVID-19 pandemic or threats to public safety.
Current customised technology solutions
Cough and sneeze detection
Utilising proprietary Deep Learning technology, NCS has been able to offer one such solution. Theorising a potential infection by people who cough and sneeze, the AI has been able to pick up behaviours such as head movement during a sneeze or an arm covering a face, especially when the person has coughed or sneezed for a prolonged period. Thereafter, the location is detected in real-time, of which a moving patrol robot sends an alert to a command centre.
Social distance monitoring
State-of-the-art edge analytics technologies such as Deep Learning, Object Detection, Wide-angled cameras, and Millimeter-wave sensors provide additional assistance to regulating social distancing in mass numbers, sounding an alert upon non-adherence. Such edge solutions run on compact and modern AI platforms which consume very little power.
Fall Prevention/ Detection
Here, we tackle the problem of patient falls in hospitals – a common but calamitous occurrence in hospital care, particularly elderly patients. More than one-third of falls in hospitals lead to injury or serious trauma, leading to additional medical bills or further health complications. In one of Singapore’s public hospitals, NCS is currently running a proof-of-concept – a demonstration of feasibility - to address this, which aims to detect, understand, and analyse patient movement behaviour when leaving a bed. Using state-of-the-art deep learning methodology, movement behaviour is analysed through time to detect and prevent a fall. This approach is performed on an AI edge device, and the patient’s face is automatically censored to protect his/her privacy.
In discussion with various local agencies on proof-of-concept opportunities – we utilise a technology that taps into existing surveillance cameras to detect fights that affect public safety – with wide-ranging utility in areas like prisons, nightlife hotspots, or schools. Employing a lightweight deep learning method, human body movements such as punching or kicking for a consistent period, are learnt to detect a fight - which thereafter automatically alerts the relevant authorities and is deployed on an AI edge device for privacy protection.
MarketsandMarkets reported that the computer vision market is expected to grow from USD 10.9 billion in 2019 to USD 17.4 billion by 2024 - growing at a CAGR of 7.8% during the forecast period. Major factors driving the market growth include:
- increasing need for quality inspection and automation
- growing demand for vision-guided robotic systems
- rising demand for application-specific computer vision systems
- increasing adoption of computer vision in autonomous vehicles, advanced cameras, image sensors, etc
- Smart camera-based computer vision systems are cost-effective, compact, and flexible – providing ease of implemented changes in these systems - based on revised regulations and standards
- Automation is widely used for assembling vehicles in this industry; with extremely high adoption of computer vision systems – contributing directly to the growth of the computer vision market in the automotive industry