Name
Abstract | ||
|---|---|---|
We propose a novel sensing technique called proactive sensing. Proactive sensing continually repositions a camera-based sensor as a way to improve hand pose estimation. Our core contribution is a scheme that effectively learns how to move the sensor to improve pose estimation confidence while requiring no ground truth hand poses. We demonstrate this concept using a low-cost rapid swing arm system built around the state-of-the-art commercial sensing system Leap Motion. The results from our user study show that proactive sensing helps estimate users' hand poses with higher confidence compared to both static and random sensing. We further present an online model update to improve performance for each user.
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Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2858036.2858587 | CHI |
Keywords | Field | DocType |
machine adaptation, hand pose estimation, learning, active | Computer vision,Sensing system,Computer science,Leap motion,Pose,Ground truth,Artificial intelligence,Swing,Online model | Conference |
ISBN | Citations | PageRank |
978-1-4503-3362-7 | 1 | 0.38 |
References | Authors | |
17 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dun-yu Hsiao | 1 | 69 | 2.37 |
| Min Sun | 2 | 1083 | 59.15 |
| Christy Ballweber | 3 | 20 | 2.93 |
| Seth Cooper | 4 | 745 | 58.65 |
| Zoran Popović | 5 | 4353 | 279.05 |