![]() ![]() Optical-flow trackers are known to incur “feature drift”. Optical-flow trackers estimate the location of a feature to be tracked by matching the image patch estimated to contain the feature in the previous image with the locally best-matching patch in the current image. The most recent version of the Camera Mouse uses an optical flow approach for tracking. The location of the feature in the camera frame is transformed into the position of the mouse pointer on the screen (Fig. The Camera Mouse tracks a small feature on a user’s face, such as a nostril or eyebrow corner. The Camera Mouse tracks head movements with a webcam and thereby enables a computer user to control the movement of the mouse pointer. Individuals, who can control their head movement, even if the movement range is very small, use systems such as the Camera Mouse as a mouse-replacement interface. If individuals with severe traumatic brain injuries, strokes, multiple sclerosis, or cerebral palsy are quadriplegic and nonverbal, they cannot use the computer with a standard keyboard and mouse, or a voice recognition system, as a communication tool.Īmong individuals with these severe impairments, the Camera Mouse has been established as an assistive communication tool in recent years. Millions of people worldwide are affected by neurological disorders that cause communication barriers. We conclude by describing how the Camera Mouse augmented with the Kernel-Subset-Tracker enabled a stroke-victim with severe motion impairments to communicate via an on-screen keyboard. Tracking of facial features was accurate, without feature drift, even during rapid head movements and extreme head orientations. Our experiments with test subjects show that augmenting the Camera Mouse with the Kernel-Subset-Tracker improves communication bandwidth statistically significantly. We propose three versions of the Kernel-Subset-Tracker, each using a different kernel, and compared their performance to the optical-flow tracker under five different experimental conditions. We designed the augmented Camera Mouse so that it can compute these templates in real time, employing kernel techniques traditionally used for classification. The Kernel-Subset-Tracker is an exemplar-based method that uses a training set of representative images to produce online templates for positional tracking. To address the problem of feature loss, we developed and incorporated the Kernel-Subset-Tracker into the Camera Mouse. The optical flow tracker may lose the facial feature when the tracked image patch drifts away from the initially-selected feature or when a user makes a rapid head movement. One such system, the Camera Mouse, uses an optical flow approach to track a manually-selected small patch of the subject’s face, such as the nostril or the edge of the eyebrow. One approach is to enable them to control the mouse pointer using head motions captured with a web camera. For these individuals, there are various mouse-replacement solutions. Some people cannot use their hands to control a computer mouse due to conditions such as cerebral palsy or multiple sclerosis. ![]()
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