Detecting Mutual Awareness Events
It is quite common that multiple human observers attend to a single static interest point. This is known as a mutual awareness event (MAWE). A preferred way to monitor these situations is with a camera that captures the human observers while using existing face detection and head pose estimation algorithms. The current work studies the underlying geometric constraints of MAWEs and reformulates them in terms of image measurements. The constraints are then used in a method that 1) detects whether such an interest point does exist, 2) determines where it is located, 3) identifies who was attending to it, and 4) reports where and when each observer was while attending to it. The method is also applied on another interesting event when a single moving human observer fixates on a single static interest point. The method can deal with the general case of an uncalibrated camera in a general environment. This is in contrast to other work on similar problems that inherently assumes a known environment or a calibrated camera. The method was tested on about 75 images from various scenes and robustly detects MAWEs and estimates their related attributes. Most of the images were found by searching the Internet.
mutual awareness head pose social signal processing sparse 3d structure