Abstract
Many researchers have utilized depth cameras for tracking user's hands to implement various interaction methods, such as touch-sensitive displays and gestural input. With the recent introduction of Microsoft's low-cost Kinect sensor, there is increased interest in this strategy. However, a review of the existing literature on these systems suggests that the majority suffer from similar limitations due to the image processing methods used to extract, segment, and relate the user's body to the environment/display. This paper presents a simple, efficient method for extracting interactions from depth images that is more flexible in terms of sensor placement, display orientation, and dependency on surface reflectivity.
Highlights
Multi-touch sensitive display surfaces and hand gesture-based interaction are increasingly popular interface methodologies
The device calculates a depth image from the distortions observed in the reflected pattern
Reviewing the methods used by other researchers to extract useful tracking information from these depth images reveals that most suffer from a few shared limitations due to assumptions or constraints in their image processing algorithms
Summary
Multi-touch sensitive display surfaces and hand gesture-based interaction are increasingly popular interface methodologies. Microsoft recently released the low-cost Kinect device, intended to be used for tracking body movements for interaction with video games. Researchers have since repurposed the Kinect as a generic depth camera to perform many different types of hand tracking and touch-enabling of display surfaces. Reviewing the methods used by other researchers to extract useful tracking information from these depth images reveals that most suffer from a few shared limitations due to assumptions or constraints in their image processing algorithms. This paper explores these limitations and presents a more flexible approach which overcomes them
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