The Camera Mouse: visual tracking of body features to provide computer access for people with severe disabilities

Betke, Margrit; Gips, James; Fleming, Paul

doi:10.1109/tnsre.2002.1021581

Cited by 421 publications

(253 citation statements)

References 20 publications

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“…A blink detector has been developed to detect drowsy drivers [41]. Facial-feature trackers [4,12,24,45] have been designed to provide video-based interfaces for people with disabilities. Eyebrow tracking is often used for determining the facial expression of the user [8,22,29].…”

Section: Introductionmentioning

confidence: 99%

Communication via eye blinks and eyebrow raises: video-based human-computer interfaces

Grauman

Betke

Lombardi

et al. 2003

Universal Access in the Information Society

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154

100

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Abstract. Two video-based human-computer interaction tools are introduced that can activate a binary switch and issue a selection command. "BlinkLink," as the first tool is called, automatically detects a user's eye blinks and accurately measures their durations. The system is intended to provide an alternate input modality to allow people with severe disabilities to access a computer. Voluntary long blinks trigger mouse clicks, while involuntary short blinks are ignored. The system enables communication using "blink patterns:" sequences of long and short blinks which are interpreted as semiotic messages. The second tool, "EyebrowClicker," automatically detects when a user raises his or her eyebrows and then triggers a mouse click. Both systems can initialize themselves, track the eyes at frame rate, and recover in the event of errors. No special lighting is required. The systems have been tested with interactive games and a spelling program. Results demonstrate overall detection accuracy of 95.6% for BlinkLink and 89.0% for EyebrowClicker.

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Section: Introductionmentioning

confidence: 99%

Communication via eye blinks and eyebrow raises: video-based human-computer interfaces

Grauman

Betke

Lombardi

et al. 2003

Universal Access in the Information Society

Self Cite

154

100

View full text Add to dashboard Cite

show abstract

“…tip of finger). In conjunction with a "spelling program" the user can "write" various words (Gips, Betke, & Fleming, 2000;Betke, Gips, & Fleming, 2002). We used 3 recordings of 5 different words.…”

Section: Experiments 1-video Tracking Datamentioning

confidence: 99%

Elastic Translation Invariant Matching of Trajectories

Vlachos¹,

Kollios

Gunopulos

2005

Mach Learn

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Abstract.We investigate techniques for analysis and retrieval of object trajectories. We assume that a trajectory is a sequence of two or three dimensional points. Trajectory datasets are very common in environmental applications, mobility experiments, video surveillance and are especially important for the discovery of certain biological patterns. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize non-metric similarity functions based on the Longest Common Subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to the similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translating of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and Dynamic Time Warping distance functions (for real and synthetic data) and show the superiority of our approach, especially under the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach.

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“…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. The Camera Mouse tracks head movements with a webcam and thereby enables a computer user to control the movement of the mouse pointer [5]. The Camera Mouse tracks a small feature on a user's face, such as a nostril or eyebrow corner.…”

Section: Introductionmentioning

confidence: 99%