Computer Vision for Active and Assisted Living

The field of computer vision has been growing steadily and attracting the interest of both researchers and the industry. Especially in the last decade, enormous advances have been made with regard to automated and reliable recognition of image or video content, such as face, object and motion recognition [1–3] and gesture and activity recognition [4, 5]. Additionally, recent advances in 3D scene acquisition, such as the Microsoft Kinect depth sensor, represent a huge leap forward, enabling 3D modelling and body pose estimation in real time with low cost and mostly simple setup solutions. Such advances are very relevant to the field of active and assisted living (AAL). Traditionally binary sensors have been employed to provide the infrastructure of a smart home upon which services can then be provided to assist people and provide comfort, safety and eHealth services, among others. However, binary sensors reach their limits when complex scenarios have to be taken into account that require a broader knowledge of what is happening and what a person is doing. One or more visual sensors can provide very detailed information about the state of the environment and its inhabitants when combined with the aforementioned pattern