A taxonomy of everyday grasps in action

Liu, Jia; Feng, Fangxiaoyu; Nakamura, Yuzuko C.; Pollard, Nancy S.

doi:10.1109/humanoids.2014.7041420

Cited by 43 publications

(40 citation statements)

References 27 publications

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“…Approach: We address the continuous problem of pose+contact+force prediction as a discrete fine-grained classification task, making use of a recent 73-class taxonomy of fine-grained hand-object interactions developed from the robotics community [28]. Our approach is inspired by prototype-based approaches for continuous shape estimation that treat the problem as a discrete categorical prediction tasks, such as shapemes [34] or poselets [5].…”

Section: Introductionmentioning

confidence: 99%

Understanding Everyday Hands in Action from RGB-D Images

Rogez¹,

Supancic

Ramanan

2015

2015 IEEE International Conference on Computer Vision (ICCV)

138

107

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International audienceWe analyze functional manipulations of handheld objects, formalizing the problem as one of fine-grained grasp classification. To do so, we make use of a recently developed fine-grained taxonomy of human-object grasps. We introduce a large dataset of 12000 RGB-D images covering 71 everyday grasps in natural interactions. Our dataset is different from past work (typically addressed from a robotics perspective) in terms of its scale, diversity, and combination of RGB and depth data. From a computer-vision perspective , our dataset allows for exploration of contact and force prediction (crucial concepts in functional grasp analysis) from perceptual cues. We present extensive experimental results with state-of-the-art baselines, illustrating the role of segmentation, object context, and 3D-understanding in functional grasp analysis. We demonstrate a near 2X improvement over prior work and a naive deep baseline, while pointing out important directions for improvement

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Section: Introductionmentioning

confidence: 99%

Understanding Everyday Hands in Action from RGB-D Images

Rogez¹,

Supancic

Ramanan

2015

2015 IEEE International Conference on Computer Vision (ICCV)

138

107

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“…Moreover, the identification of the most recurrent grasp types has important applications in robotics, biomechanics, upper limb rehabilitation, and HCI. Thus, several taxonomies were proposed in the past decades [110], [112], [113], [114], [115], [116], [117]. For a comprehensive comparison among these taxonomies, the reader is referred to [110].…”

Section: Hand Grasp Analysis and Gesture Recognitionmentioning

confidence: 99%

Analysis of the Hands in Egocentric Vision: A Survey

Bandini

Zariffa

2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Egocentric vision (a.k.a. first-person vision -FPV) applications have thrived over the past few years, thanks to the availability of affordable wearable cameras and large annotated datasets. The position of the wearable camera (usually mounted on the head) allows recording exactly what the camera wearers have in front of them, in particular hands and manipulated objects. This intrinsic advantage enables the study of the hands from multiple perspectives: localizing hands and their parts within the images; understanding what actions and activities the hands are involved in; and developing human-computer interfaces that rely on hand gestures. In this survey, we review the literature that focuses on the hands using egocentric vision, categorizing the existing approaches into: localization (where are the hands or part of them?); interpretation (what are the hands doing?); and application (e.g., systems that used egocentric hand cues for solving a specific problem). Moreover, a list of the most prominent datasets with hand-based annotations is provided.

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“…In the 1990s, Kang and Ikeuchi [15], [16], [17], [2], [3] presented an important paradigm of using the classification of human grasps (power and precision grasps) to help automate robotic manipulation. More recent work has utilized large amounts of video/image data to understand the scope of grasps [5] and the complexity of everyday object interactions [6].…”

Section: A Hand Grasp Analysismentioning

confidence: 99%

“…The study of human hand usage has been a topic of longstanding interest in the robotics community [1], [2], [3], [4], [5], [6] where research results are typically obtained through many hours of visual observation and thoughtful introspection. Recently, supervised [7] and unsupervised [8] computer vision-based approaches have been proposed in an effort to automate the process of gathering hand use statistics.…”

Section: Introductionmentioning

confidence: 99%

Hand parsing for fine-grained recognition of human grasps in monocular images

Saran

Teney

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We propose a novel method for performing finegrained recognition of human hand grasp types using a single monocular image to allow computational systems to better understand human hand use. In particular, we focus on recognizing challenging grasp categories which differ only by subtle variations in finger configurations. While much of the prior work on understanding human hand grasps has been based on manual detection of grasps in video, this is the first work to automate the analysis process for fine-grained grasp classification. Instead of attempting to utilize a parametric model of the hand, we propose a hand parsing framework which leverages a data-driven learning to generate a pixelwise segmentation of a hand into finger and palm regions. The proposed approach makes use of appearance-based cues such as finger texture and hand shape to accurately determine hand parts. We then build on the hand parsing result to compute high-level grasp features to learn a supervised fine-grained grasp classifier. To validate our approach, we introduce a grasp dataset recorded with a wearable camera, where the hand and its parts have been manually segmented with pixel-wise accuracy. Our results show that our proposed automatic hand parsing technique can improve grasp classification accuracy by over 30 percentage points over a state-of-the-art grasp recognition technique.

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A taxonomy of everyday grasps in action

Cited by 43 publications

References 27 publications

Understanding Everyday Hands in Action from RGB-D Images

Understanding Everyday Hands in Action from RGB-D Images

Analysis of the Hands in Egocentric Vision: A Survey

Hand parsing for fine-grained recognition of human grasps in monocular images

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