Egocentric Prediction of Action Target in 3D

• URL: http://arxiv.org/abs/2203.13116v1
• Date: Thu, 24 Mar 2022 15:16:05 GMT
• Title: Egocentric Prediction of Action Target in 3D
• Authors: Yiming Li and Ziang Cao and Andrew Liang and Benjamin Liang and Luoyao Chen and Hang Zhao and Chen Feng
• Abstract summary: We propose a large multimodality dataset of more than 1 million frames of RGB-D and IMU streams to stimulate more research on this challenging egocentric vision task. Our results demonstrate that this new task is worthy of further study by researchers in robotics, vision, and learning communities.
• Score: 17.99025294221712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We are interested in anticipating as early as possible the target location of a person's object manipulation action in a 3D workspace from egocentric vision. It is important in fields like human-robot collaboration, but has not yet received enough attention from vision and learning communities. To stimulate more research on this challenging egocentric vision task, we propose a large multimodality dataset of more than 1 million frames of RGB-D and IMU streams, and provide evaluation metrics based on our high-quality 2D and 3D labels from semi-automatic annotation. Meanwhile, we design baseline methods using recurrent neural networks and conduct various ablation studies to validate their effectiveness. Our results demonstrate that this new task is worthy of further study by researchers in robotics, vision, and learning communities.

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