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2D Human Pose Estimation: New Benchmark and State of the Art Analysis
TLDR
A novel benchmark "MPII Human Pose" is introduced that makes a significant advance in terms of diversity and difficulty, a contribution that is required for future developments in human body models. Expand
A database for fine grained activity detection of cooking activities
TLDR
A novel database of 65 cooking activities, continuously recorded in a realistic setting, is proposed, suggesting that fine-grained activities are more difficult to detect and the body model can help in those cases. Expand
Monocular 3D pose estimation and tracking by detection
TLDR
A three-stage process building on a number of recent advances to recover 3D human pose from monocular image sequences and demonstrates state-of-the-art performance on the HumanEva II benchmark, and shows the applicability of the approach to articulated 3D tracking in realistic street conditions. Expand
People-tracking-by-detection and people-detection-by-tracking
TLDR
This paper combines the advantages of both detection and tracking in a single framework using a hierarchical Gaussian process latent variable model (hGPLVM) and presents experimental results that demonstrate how this allows to detect and track multiple people in cluttered scenes with reoccurring occlusions. Expand
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
TLDR
An approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other is proposed. Expand
Pictorial structures revisited: People detection and articulated pose estimation
TLDR
This paper proposes a generic approach based on the pictorial structures framework, and demonstrates that such a model is equally suitable for both detection and pose estimation tasks, outperforming the state of the art on three recently proposed datasets. Expand
DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body partExpand
End-to-End People Detection in Crowded Scenes
TLDR
This work proposes a model that is based on decoding an image into a set of people detections, which takes an image as input and directly outputs aset of distinct detection hypotheses. Expand
Pictorial structures revisited: People detection and articulated pose estimation
TLDR
This paper proposes a generic approach based on the pictorial structures framework, and demonstrates that such a model is equally suitable for both detection and pose estimation tasks, outperforming the state of the art on three recently proposed datasets. Expand
Multiple People Tracking by Lifted Multicut and Person Re-identification
TLDR
A novel graph-based formulation that links and clusters person hypotheses over time by solving an instance of a minimum cost lifted multicut problem and is reported a new state-of-the-art for the MOT16 benchmark. Expand
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