AI image recognition: Driving our cars, keeping us healthy, protecting our public spaces
The past decade has seen great strides taken in the field of image recognition in video content. AI can accurately identify simple activities such as cycling, doing a pull-up or ice skating in short video clips featuring a limited number of people. But there is as yet no way to reach the same level of accuracy in longer video streams where multiple people interact simultaneously and are involved in more complex causal activities. Professor Cees Snoek and his team at the University of Amsterdam (UvA) are working on a range of projects in this area.
With so many potential applications for automated video understanding – for example in self-driving cars, cashier-free retail, or content-moderation in social media – researchers around the world are hard at work on optimising these technologies, which are increasingly finding their way into our everyday lives. At the UvA, Snoek and his team are currently working on various projects that illustrate the way these technologies can be used in practice.
The team’s ‘healthcare’ project involves the UvA spin-off Kepler Vision Technologies. Kepler makes use of the world’s first-ever body language recognition software. The software looks into video streams and can recognise a human’s body language, poses and actions. Kepler uses this ability to produce applications intended for use in care for the elderly. Elderly care is intensive for nurses, with skilled care workers often thoroughly overworked. Kepler’s software helps by monitoring the clients and recognising when they need care. For example, the Kepler Night Nurse can recognise when a client is struggling, or cannot get out of bed, or, equally, if a client has remained in the bathroom for a concerning amount of time. It can also distinguish between someone lying on the floor because of a fall and someone lying on a couch to rest. If the Night Nurse spots one of these potential problems it sends a notification, enabling care home nurses to avoid unnecessary control rounds. In the future, the software will also be able to monitor whether someone is eating and drinking enough or is at risk of becoming socially isolated.
You can read more about the projects on the UvA website.