An approach for designing and evaluating a plug-in vision-based tabletop touch identification system

Key functionality for interactive tabletops to provide effective collaboration affordances requires touch identification, where each touch is matched to the right user. This can be valuable to provide adaptive functions, personalisation of content, collaborative gestures and capture of differentiated interaction for real-time or further analysis. While there is increased attention on touch-identification mechanisms, currently there is no developed solution to readily enhance available tabletop hardware to include such functionality. This paper proposes a plug-in system that adds touch identification to a conventional tabletop. It also presents an analysis tool and the design of an evaluation suite to inform application designers of the effectiveness of the system to differentiate users. We illustrate its use by evaluating the solution under a number of conditions of: scalability (number of users); activity density; and multi-touch gestures. Our contributions are: (1) an offthe- shelf system to add user differentiation and tracking to currently available interactive tabletop hardware; and (2) the foundations for systematic assessment of touch identification accuracy for vision-based systems.