Controlling your TV with gestures

Chen, Mingyu; Mummert, Lily B.; Pillai, Padmanabhan; Hauptmann, Alexander G.; Sukthankar, Rahul

doi:10.1145/1743384.1743453

Cited by 39 publications

(17 citation statements)

References 21 publications

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“…a grab gesture). Chen et al [5] dispensed with the visible cursor and controlled TV channel and volume settings by moving the left or the right arm upwards or downwards. Dezfuli et al [9] assigned interactions to specific regions of the users´ non-dominant hand, triggered when tapped with the dominant hand which facilitates to control the TV blindly.…”

Section: Freehand Gesturesmentioning

confidence: 99%

Tablet, gestures, remote control?

Bobeth

Schrammel

Deutsch

et al. 2014

Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video

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Due to recent development of TVs in the direction of highly interactive multimedia platforms, interactive TV (iTV) applications gain popularity. In terms of control possibilities a variety of input modalities have become available, though effects on performance and user experience of different age groups when controlling different iTV applications remain unclear. We present an empirical investigation comparing three input modalities (tablet, freehand gestures, remote) for controlling two iTV applications (Photo Browser, Nutrition Tracker) used by older and younger adults. Results show that all three independent variables had significant influence on performance, while we did not find influence of age or application on user experience. Overall tablet input based on a mirrored TV screen showed the best performance and was preferred by both age groups. Older adults were overall slower and showed a particularly large performance gap with the remote in comparison to younger adults.

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Section: Freehand Gesturesmentioning

confidence: 99%

Tablet, gestures, remote control?

Bobeth

Schrammel

Deutsch

et al. 2014

Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video

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“…The second application, TV control, provides an interface to control a television via gestures [5]. Each video frame is sent to two separate tasks, face detection and motion extraction, shown in Figure 2.…”

Section: Interactive Perception Applicationsmentioning

confidence: 99%

Incremental placement of interactive perception applications

Yigitbasi

Mummert

Pillai

et al. 2011

Proceedings of the 20th International Symposium on High Performance Distributed Computing

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Interactive perception applications, such as gesture recognition and vision-based user interfaces, process high-data rate streams with compute intensive computer vision and machine learning algorithms. These applications can be represented as data flow graphs comprising several processing stages. Such applications require low latency to be interactive so that the results are immediately available to the user. To achieve low latency, we exploit the inherent coarsegrained task and data parallelism of these applications by running them on clusters of machines. This paper addresses an important problem that arises: how to place the stages of these applications on machines to minimize the latency, and in particular, how to adjust an existing schedule in response to changes in the operating conditions (perturbations) while minimizing the disruption in the existing placement (churn). To this end, we propose four incremental placement heuristics which use the HEFT scheduling algorithm as their primary building block. Through simulations and experiments on a real implementation, using diverse workloads and a range of perturbation scenarios, we demonstrate that dynamic adjustment of the schedule can improve latency by as much as 36%, while producing little churn.

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“…These include a gesturebased gaming system, a natural pointing interface [3], a gesture-based tv control application [10], object pose detection [2], and video action recognition [1] (see Figure 1). Key takeaways from this work are that it is indeed possible to run latency-sensitive interactive perception applications on a cluster of machines, and that there is sufficient coarse-grained parallelism in these applications to make good use of 10s to 100s of processor cores.…”

Section: A Programming Framework For Distributed Interactive Applicamentioning

confidence: 99%

Running Interactive Perception Applications on Open Cirrus

Zhu

Yigitbasi

Pillai

2011

2011 Sixth Open Cirrus Summit

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Interactive perception applications, such as gesture recognition and vision-based user interfaces, process highdata rate streams with compute intensive computer vision and machine learning algorithms. Yet, they require extremely low latencies to remain interactive and ensure timely results to users. Cluster computing resources, such as those provided by Open Cirrus deployments, can help address the computation requirements, but significant challenges exist in practice. This paper highlights our efforts to parallelize interactive perception applications, tune them for best fidelity and latency, and place, schedule, and execute them on a cluster platform. We also look at remaining open problems and potential solutions.

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Controlling your TV with gestures

Cited by 39 publications

References 21 publications

Tablet, gestures, remote control?

Tablet, gestures, remote control?

Incremental placement of interactive perception applications

Running Interactive Perception Applications on Open Cirrus

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