PoseTrack: Joint Multi-person Pose Estimation and Tracking
Umar Iqbal,Anton Milan,Juergen Gall +2 more
- pp 4654-4663
TLDR
This work proposes a novel method that jointly models multi-person pose estimation and tracking in a single formulation and introduces a challenging Multi-Person PoseTrack dataset, and proposes a completely unconstrained evaluation protocol that does not make any assumptions about the scale, size, location or the number of persons.Abstract:
In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos. Existing methods for multi-person pose estimation in images cannot be applied directly to this problem, since it also requires to solve the problem of person association over time in addition to the pose estimation for each person. We therefore propose a novel method that jointly models multi-person pose estimation and tracking in a single formulation. To this end, we represent body joint detections in a video by a spatio-temporal graph and solve an integer linear program to partition the graph into sub-graphs that correspond to plausible body pose trajectories for each person. The proposed approach implicitly handles occlusion and truncation of persons. Since the problem has not been addressed quantitatively in the literature, we introduce a challenging Multi-Person PoseTrack dataset, and also propose a completely unconstrained evaluation protocol that does not make any assumptions about the scale, size, location or the number of persons. Finally, we evaluate the proposed approach and several baseline methods on our new dataset.read more
Citations
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Proceedings ArticleDOI
Deep High-Resolution Representation Learning for Human Pose Estimation
TL;DR: This paper proposes a network that maintains high-resolution representations through the whole process of human pose estimation and empirically demonstrates the effectiveness of the network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.
Journal ArticleDOI
DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
Jacob M. Graving,Daniel Chae,Hemal Naik,Liang Li,Liang Li,Benjamin Koger,Benjamin Koger,Blair R. Costelloe,Blair R. Costelloe,Iain D. Couzin,Iain D. Couzin +10 more
TL;DR: A new easy-to-use software toolkit, DeepPoseKit, is introduced that addresses animal pose estimation problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision.
Journal ArticleDOI
Survey on Emotional Body Gesture Recognition
Fatemeh Noroozi,Ciprian A. Corneanu,Dorota Kamińska,Tomasz Sapiński,Sergio Escalera,Gholamreza Anbarjafari +5 more
TL;DR: In this paper, the authors present a comprehensive survey of body gesture recognition methods and discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition, and define a complete framework for automatic emotional body gestures recognition.
Journal ArticleDOI
Monocular human pose estimation: A survey of deep learning-based methods
TL;DR: This survey extensively reviews the recent deep learning-based 2D and 3D human pose estimation methods published since 2014 and summarizes the challenges, main frameworks, benchmark datasets, evaluation metrics, performance comparison, and discusses some promising future research directions.
Proceedings ArticleDOI
ArtTrack: Articulated Multi-Person Tracking in the Wild
Eldar Insafutdinov,Mykhaylo Andriluka,Leonid Pishchulin,Siyu Tang,Evgeny Levinkov,Bjoern Andres,Bernt Schiele +6 more
TL;DR: In this article, the authors propose an approach for articulated tracking of multiple people in unconstrained videos, which is based on a model that resembles existing architectures for single-frame pose estimation but is substantially faster.
References
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Posted Content
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Proceedings ArticleDOI
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TL;DR: In this paper, a convolutional network is incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation, which can implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation.