Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework

• URL: http://arxiv.org/abs/2203.04113v1
• Date: Tue, 8 Mar 2022 14:35:46 GMT
• Title: Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework
• Authors: Mehwish Ghafoor, Arif Mahmood
• Abstract summary: The proposed method estimates more accurate 3D human poses using 2D skeletons with missing joints as input. Our experiments demonstrate the effectiveness of the proposed framework for handling the missing joints.
• Score: 11.509692423756448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D human pose estimation using monocular images is an important yet challenging task. Existing 3D pose detection methods exhibit excellent performance under normal conditions however their performance may degrade due to occlusion. Recently some occlusion aware methods have also been proposed, however, the occlusion handling capability of these networks has not yet been thoroughly investigated. In the current work, we propose an occlusion-guided 3D human pose estimation framework and quantify its occlusion handling capability by using different protocols. The proposed method estimates more accurate 3D human poses using 2D skeletons with missing joints as input. Missing joints are handled by introducing occlusion guidance that provides extra information about the absence or presence of a joint. Temporal information has also been exploited to better estimate the missing joints. A large number of experiments are performed for the quantification of occlusion handling capability of the proposed method on three publicly available datasets in various settings including random missing joints, fixed body parts missing, and complete frames missing, using mean per joint position error criterion. In addition to that, the quality of the predicted 3D poses is also evaluated using action classification performance as a criterion. 3D poses estimated by the proposed method achieved significantly improved action recognition performance in the presence of missing joints. Our experiments demonstrate the effectiveness of the proposed framework for handling the missing joints as well as quantification of the occlusion handling capability of the deep neural networks.

Related papers

• Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation [69.49430149980789]
  We show that our proposed method compares favorably with the state-of-the-art (SoA) Our experimental results also reveal that in the absence of any occlusion handling mechanism, the performance of SoA 3D HPE methods degrades significantly when they encounter occlusion.
  arXiv  Detail & Related papers  (2023-04-24T13:05:13Z)
• Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation [33.86986028882488]
  Occlusion poses a great threat to monocular multi-person 3D human pose estimation due to large variability in terms of the shape, appearance, and position of occluders. Existing methods try to handle occlusion with pose priors/constraints, data augmentation, or implicit reasoning. We develop a method to explicitly model this process that significantly improves bottom-up multi-person human pose estimation.
  arXiv  Detail & Related papers  (2022-07-29T22:12:50Z)
• Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation [70.32536356351706]
  We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations. We derive suitable measures to quantify prediction uncertainty at both pose and joint level. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-29T07:14:58Z)
• PONet: Robust 3D Human Pose Estimation via Learning Orientations Only [116.1502793612437]
  We propose a novel Pose Orientation Net (PONet) that is able to robustly estimate 3D pose by learning orientations only. PONet estimates the 3D orientation of these limbs by taking advantage of the local image evidence to recover the 3D pose. We evaluate our method on multiple datasets, including Human3.6M, MPII, MPI-INF-3DHP, and 3DPW.
  arXiv  Detail & Related papers  (2021-12-21T12:48:48Z)
• Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos [33.974241749058585]
  We propose a novel framework integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs) to robustly estimate camera-centric multi-person 3D poses. In particular, we introduce a human-joint GCN, which employs the 2D pose estimator's confidence scores to improve the pose estimation results. The two GCNs work together to estimate the spatial frame-wise 3D poses and can make use of both visible joint and bone information in the target frame to estimate the occluded or missing human-part information.
  arXiv  Detail & Related papers  (2020-12-22T03:01:19Z)
• Multi-Scale Networks for 3D Human Pose Estimation with Inference Stage Optimization [33.02708860641971]
  Estimating 3D human poses from a monocular video is still a challenging task. Many existing methods drop when the target person is cluded by other objects, or the motion is too fast/slow relative to the scale and speed of the training data. We introduce atemporal-temporal network for robust 3D human pose estimation.
  arXiv  Detail & Related papers  (2020-10-13T15:24:28Z)
• Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry [62.29762409558553]
  Epipolar constraints are at the core of feature matching and depth estimation in multi-person 3D human pose estimation methods. Despite the satisfactory performance of this formulation in sparser crowd scenes, its effectiveness is frequently challenged under denser crowd circumstances. In this paper, we depart from the multi-person 3D pose estimation formulation, and instead reformulate it as crowd pose estimation.
  arXiv  Detail & Related papers  (2020-07-21T17:59:36Z)
• Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation [58.72192168935338]
  Generalizability of human pose estimation models developed using supervision on large-scale in-studio datasets remains questionable. We propose a novel kinematic-structure-preserved unsupervised 3D pose estimation framework, which is not restrained by any paired or unpaired weak supervisions. Our proposed model employs three consecutive differentiable transformations named as forward-kinematics, camera-projection and spatial-map transformation.
  arXiv  Detail & Related papers  (2020-06-24T23:56:33Z)
• Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis [72.34794624243281]
  We propose a self-supervised learning framework to disentangle variations from unlabeled video frames. Our differentiable formalization, bridging the representation gap between the 3D pose and spatial part maps, allows us to operate on videos with diverse camera movements.
  arXiv  Detail & Related papers  (2020-04-09T07:55:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.