Multi-person Pose Estimation with Local Joint-to-Person Associations

Iqbal, Umar; Gall, Jüergen

doi:10.1007/978-3-319-48881-3_44

Cited by 123 publications

(91 citation statements)

References 28 publications

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“…Moreover, it is natural that persons occlude each other during interactions, and may also become partially truncated to various degrees. Multi-person pose estimation has therefore gained much attention recently [11,37,31,43,23,12,8,3,30,16,17]. Earlier methods in this direction follow a two-staged approach [31,12,8] by first detecting the persons in an image followed by a human pose estimation technique for each person individually.…”

Section: Related Workmentioning

confidence: 99%

“…The approach has been further improved by stronger part detectors and efficient approximations [16]. The approach in [17] also proposes a simplification of [30] by tackling the problem locally for each person. However, it still relies on a separate person detector.…”

Section: Related Workmentioning

confidence: 99%

“…We on the other hand propose to simultaneously estimate the pose of multiple persons and track them over time. To this end, we build upon the recent progress on multi-person pose estimation in images [30,16,17] and propose a joint objective for both problems.…”

Section: Related Workmentioning

confidence: 99%

“…The field of human pose estimation in images has progressed remarkably over the past few years. The methods have advanced from pose estimation of single pre-localized persons [30,6,40,14,16,27,5,32] to the more challenging and realistic case of multiple, potentially overlapping and truncated persons [12,8,30,16,17]. Many applications, such as mentioned before, however, aim to analyze human body motion over time.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to previous works, we aim to solve the association of each person across the video and the pose estimation together. To this end, we build upon the recent methods for multiperson pose estimation in images [30,16,17] that build a spatial graph based on joint proposals to estimate the pose of multiple persons in an image. In particular, we cast the problem as an optimization of a densely connected spatiotemporal graph connecting body joint candidates spatially as well as temporally.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

PoseTrack: Joint Multi-person Pose Estimation and Tracking

Iqbal

Milan

Gall

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

178

146

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PoseTrack: Joint Multi-person Pose Estimation and Tracking

Iqbal

Milan

Gall

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

178

146

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Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World

Fabbri

Lanzi

Calderara

et al. 2018

Computer Vision – ECCV 2018

129

100

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Multi-PeopleTracking in an open-world setting requires a special effort in precise detection. Moreover, temporal continuity in the detection phase gains more importance when scene cluttering introduces the challenging problems of occluded targets. For the purpose, we propose a deep network architecture that jointly extracts people body parts and associates them across short temporal spans. Our model explicitly deals with occluded body parts, by hallucinating plausible solutions of not visible joints. We propose a new end-to-end architecture composed by four branches (visible heatmaps, occluded heatmaps, part affinity fields and temporal affinity fields) fed by a time linker feature extractor. To overcome the lack of surveillance data with tracking, body part and occlusion annotations we created the vastest Computer Graphics dataset for people tracking in urban scenarios by exploiting a photorealistic videogame. It is up to now the vastest dataset (about 500.000 frames, almost 10 million body poses) of human body parts for people tracking in urban scenarios. Our architecture trained on virtual data exhibits good generalization capabilities also on public real tracking benchmarks, when image resolution and sharpness are high enough, producing reliable tracklets useful for further batch data association or re-id modules.

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Pose Partition Networks for Multi-person Pose Estimation

Nie

Feng

Yan

2018

Computer Vision – ECCV 2018

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This paper proposes a new Generative Partition Network (GPN) to address the challenging multi-person pose estimation problem. Different from existing models that are either completely top-down or bottom-up, the proposed GPN introduces a novel strategy-it generates partitions for multiple persons from their global joint candidates and infers instance-specific joint configurations simultaneously. The GPN is favorably featured by low complexity and high accuracy of joint detection and re-organization. In particular, GPN designs a generative model that performs one feed-forward pass to efficiently generate robust person detections with joint partitions, relying on dense regressions from global joint candidates in an embedding space parameterized by centroids of persons. In addition, GPN formulates the inference procedure for joint configurations of human poses as a graph partition problem, and conducts local optimization for each person detection with reliable global affinity cues, leading to complexity reduction and performance improvement. GPN is implemented with the Hourglass architecture as the backbone network to simultaneously learn joint detector and dense regressor. Extensive experiments on benchmarks MPII Human Pose Multi-Person, extended PASCAL-Person-Part, and WAF, show the efficiency of GPN with new state-of-the-art performance.

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Multi-person Pose Estimation with Local Joint-to-Person Associations

Cited by 123 publications

References 28 publications

PoseTrack: Joint Multi-person Pose Estimation and Tracking

PoseTrack: Joint Multi-person Pose Estimation and Tracking

Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World

Pose Partition Networks for Multi-person Pose Estimation

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