PoseAnalyser: A Survey on Human Pose Estimation

Kulkarni, Sakshi; Deshmukh, S. M.; Fernandes, Favin; Patil, Aniket; Jabade, Vaishali

doi:10.1007/s42979-022-01567-2

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…1) Keypoint-based 3D Skeleton Estimation: Keypointbased human pose estimation relies on sparse, 2D keypoint detections that are either "lifted" to 3D or triangulated using multi-view geometry. For a more in-depth review, Kulkarni et al recently published an extensive survey article [10]. Here, only the directly related and relevant literature is presented.…”

Section: B Human State Estimation In 3dmentioning

confidence: 99%

BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking

Henning

Laidlow

Leutenegger

2022

Lecture Notes in Computer Science

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Robust, fast, and accurate human state -6D pose and posture -estimation remains a challenging problem. For real-world applications, the ability to estimate the human state in real-time is highly desirable. In this paper, we present BodySLAM++, a fast, efficient, and accurate human and camera state estimation framework relying on visualinertial data. BodySLAM++ extends an existing visual-inertial state estimation framework, OKVIS2, to solve the dual task of estimating camera and human states simultaneously. Our system improves the accuracy of both human and camera state estimation with respect to baseline methods by 26% and 12%, respectively, and achieves real-time performance at 15+ frames per second on an Intel i7-model CPU. Experiments were conducted on a custom dataset containing both ground truth human and camera poses collected with an indoor motion tracking system.

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Section: B Human State Estimation In 3dmentioning

confidence: 99%

BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking

Henning

Laidlow

Leutenegger

2022

Lecture Notes in Computer Science

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“…There are two common approaches to pose estimation: top-down and bottomup. The top-down approach detects humans by finding bounding boxes of individuals and then estimating the keypoints within each bounding box [15,23]. It has achieved SOTA performance, however the computation scales with the number of detected humans and it struggles with occlusion [32].…”

Section: Introductionmentioning

confidence: 99%

Improving Visual Perception of a Social Robot for Controlled and In-the-wild Human-robot Interaction

Zhong,

Tian,

et al. 2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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Social robots often rely on visual perception to understand their users and the environment. Recent advancements in data-driven approaches for computer vision have demonstrated great potentials for applying deep-learning models to enhance a social robot's visual perception. However, the high computational demands of deep-learning methods, as opposed to the more resource-efficient shallow-learning models, bring up important questions regarding their effects on real-world interaction and user experience. It is unclear how will the objective interaction performance and subjective user experience be influenced when a social robot adopts a deep-learning based visual perception model. We employed stateof-the-art human perception and tracking models to improve the visual perception function of the Pepper robot and conducted a controlled lab study and an in-the-wild human-robot interaction study to evaluate this novel perception function for following a specific user with other people present in the scene.

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