Difference between revisions of "Gaze and Intent"
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The ability of an autonomous system to understand something about a human’s intent is important to the success of many systems that involve both humans and autonomous agents. In this work, we consider the specific setting of a human passenger riding in an autonomous vehicle, where the passenger intends to go to or learn about a specific point of interest along the vehicle’s route. In this setting, we seek to provide the vehicle with the ability to infer this point of interest using real-time gaze information. This is a difficult problem in that the inference must be designed in the context of the moving vehicle, i.e., in a dynamic environment with dynamic interest points. We propose here a solution to this problem via a novel methodology called Dynamic Interest Point Detection (DIPD) for inferring the point of interest corresponding to the human’s intent using gaze tracking data and a dynamic Markov Random Field (MRF) model. The energy function we develop allows the algorithm to successfully filter out noise from the eye tracker, such as eye blinks, high-speed tracking misalignment, and other sources of error. We demonstrate the success of this DIPD technique experimentally and show that it achieves up to a 28% increase in inference success compared to a nearest-neighbor approach. | The ability of an autonomous system to understand something about a human’s intent is important to the success of many systems that involve both humans and autonomous agents. In this work, we consider the specific setting of a human passenger riding in an autonomous vehicle, where the passenger intends to go to or learn about a specific point of interest along the vehicle’s route. In this setting, we seek to provide the vehicle with the ability to infer this point of interest using real-time gaze information. This is a difficult problem in that the inference must be designed in the context of the moving vehicle, i.e., in a dynamic environment with dynamic interest points. We propose here a solution to this problem via a novel methodology called Dynamic Interest Point Detection (DIPD) for inferring the point of interest corresponding to the human’s intent using gaze tracking data and a dynamic Markov Random Field (MRF) model. The energy function we develop allows the algorithm to successfully filter out noise from the eye tracker, such as eye blinks, high-speed tracking misalignment, and other sources of error. We demonstrate the success of this DIPD technique experimentally and show that it achieves up to a 28% increase in inference success compared to a nearest-neighbor approach. | ||
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| + | '''[https://drive.google.com/file/d/1UHAfu5LuvwpMmznp8Lvu2O441cBuILF-/view?usp=sharing Example Video]''' | ||
Latest revision as of 10:45, 21 November 2017
The ability of an autonomous system to understand something about a human’s intent is important to the success of many systems that involve both humans and autonomous agents. In this work, we consider the specific setting of a human passenger riding in an autonomous vehicle, where the passenger intends to go to or learn about a specific point of interest along the vehicle’s route. In this setting, we seek to provide the vehicle with the ability to infer this point of interest using real-time gaze information. This is a difficult problem in that the inference must be designed in the context of the moving vehicle, i.e., in a dynamic environment with dynamic interest points. We propose here a solution to this problem via a novel methodology called Dynamic Interest Point Detection (DIPD) for inferring the point of interest corresponding to the human’s intent using gaze tracking data and a dynamic Markov Random Field (MRF) model. The energy function we develop allows the algorithm to successfully filter out noise from the eye tracker, such as eye blinks, high-speed tracking misalignment, and other sources of error. We demonstrate the success of this DIPD technique experimentally and show that it achieves up to a 28% increase in inference success compared to a nearest-neighbor approach.