Learning-by-Synthesis for Appearance-Based 3D Gaze Estimation

Sugano, Yusuke; Matsushita, Yasuyuki; Sato, Yoichi

doi:10.1109/cvpr.2014.235

Cited by 328 publications

(310 citation statements)

References 23 publications

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“…However, the whole-body appearance has a wide variety of patterns (body orientation or spine angle, in our case) even when the camera view is restricted. Also gaze direction estimation from the eye-region appearance is explored with conditional regression forests conditioned on the head pose [50]. For nonarticulated objects, regression-based pose estimation [51] can be done.…”

Section: Pose Regressionmentioning

confidence: 99%

Upper Body Pose Estimation for Team Sports Videos Using a Poselet-Regressor of Spine Pose and Body Orientation Classifiers Conditioned by the Spine Angle Prior

Oshima

Tanabiki

Aoki

2015

IPSJ Transactions on Computer Vision and Applications

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Abstract:We propose a per-frame upper body pose estimation method for sports players captured in low-resolution team sports videos. Using the head-center-aligned upper body region appearance in each frame from the head tracker, our framework estimates (1) 2D spine pose, composed of the head center and the pelvis center locations, and (2) the orientation of the upper body in each frame. Our framework is composed of three steps. In the first step, the head region of the subject player is tracked with a standard tracking-by-detection technique for upper body appearance alignment. In the second step, the relative pelvis center location from the head center is estimated by our newly proposed poseletregressor in each frame to obtain spine angle priors. In the last step, the body orientation is estimated by the upper body orientation classifier selected by the spine angle range. Owing to the alignment of the body appearance and the usage of multiple body orientation classifiers conditioned by the spine angle prior, our method can robustly estimate the body orientation of a player with a large variation of visual appearances during a game, even during side-poses or self-occluded poses. We tested the performance of our method in both American football and soccer videos.

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Section: Pose Regressionmentioning

confidence: 99%

Upper Body Pose Estimation for Team Sports Videos Using a Poselet-Regressor of Spine Pose and Body Orientation Classifiers Conditioned by the Spine Angle Prior

Oshima

Tanabiki

Aoki

2015

IPSJ Transactions on Computer Vision and Applications

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show abstract

“…In general, gaze estimation methods fall into two categories: (1) appearance-based methods (Hansen et al 2002;Lu et al 2011;Valenti and Gevers 2012;Sugano et al 2014) and (2) 3D-eye model-based methods (Villanueva et al 2006;Eizenman 2006, 2008;Chen and Ji 2011;Draelos et al 2015;Xiong et al 2015). The former class extracts features from images of the eyes and map them to points on the gaze plane (i.e.…”

Section: Introductionmentioning

confidence: 99%

Auto-Calibrated Gaze Estimation Using Human Gaze Patterns

et al. 2017

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We present a novel method to auto-calibrate gaze estimators based on gaze patterns obtained from other viewers. Our method is based on the observation that the gaze patterns of humans are indicative of where a new viewer will look at. When a new viewer is looking at a stimulus, we first estimate a topology of gaze points (initial gaze points). Next, these points are transformed so that they match the gaze patterns of other humans to find the correct gaze points. In a flexible uncalibrated setup with a web camera and no chin rest, the proposed method is tested on ten subjects and ten images. The method estimates the gaze points after looking at a stimulus for a few seconds with an average error below 4.5 • . Although the reported performance is lower than what could be achieved with dedicated hardware or calibrated setup, the proposed method still provides sufficient accuracy to trace the viewer attention. This is promising considering the fact that auto-calibration is done in a flexible setup , without the use of a chin rest, and based only on a few seconds of gaze initialization data. To the best of our knowledge, this is the first work to use human gaze patterns in order to auto-calibrate gaze estimators.

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“…These techniques require head and pupil position estimation in order to track gaze accurately. Most of these approaches require hardware configurations to obtain head pose invariance with most of them to be feature or appearance based, [10,24,25,19,22,18,12].…”

Section: Introductionmentioning

confidence: 99%