We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

In this paper, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process. We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset. In addition, we show the superiority of our network in pose tracking on the PoseTrack dataset. The code and models have been publicly available at https://github.com/leoxiaobin/deep-high-resolution-net.pytorch.

/pdf/deep-high-resolution-representation-learning-for-human-pose-145hgz2sfq.pdf

Deep High-Resolution Representation Learning for Human Pose Estimation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time.

Objects as Points

High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions \emph{in series} (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams \emph{in parallel}; (ii) Repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at~{\url{this https URL}}.

Deep High-Resolution Representation Learning for Visual Recognition

Multi-person pose estimation in the wild is challenging. Although state-of-the-art human detectors have demonstrated good performance, small errors in localization and recognition are inevitable. These errors can cause failures for a single-person pose estimator (SPPE), especially for methods that solely depend on human detection results. In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes. Our framework consists of three components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). Our method is able to handle inaccurate bounding boxes and redundant detections, allowing it to achieve 76:7 mAP on the MPII (multi person) dataset[3]. Our model and source codes are made publicly available.

RMPE: Regional Multi-person Pose Estimation

Multi-person pose estimation in the wild is challenging. Although state-of-the-art human detectors have demonstrated good performance, small errors in localization and recognition are inevitable. These errors can cause failures for a single-person pose estimator (SPPE), especially for methods that solely depend on human detection results. In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes. Our framework consists of three components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). Our method is able to handle inaccurate bounding boxes and redundant detections, allowing it to achieve a 17% increase in mAP over the state-of-the-art methods on the MPII (multi person) dataset.Our model and source codes are publicly available.

Recent advances in multi-stage algorithms have shown great promise, but two important problems still remain. First of all, at inference time, information can't feed back from downstream to upstream. Second, at training time, end-to-end training is not possible if the overall pipeline involves non-differentiable functions, and so different stages can't be jointly optimized. In this paper, we propose a novel environment upgrade reinforcement learning framework to solve the feedback and joint optimization problems. Our framework re-links the downstream stage to the upstream stage by a reinforcement learning agent. While training the agent to improve final performance by refining the upstream stage's output, we also upgrade the downstream stage (environment) according to the agent's policy. In this way, agent policy and environment are jointly optimized. We propose a training algorithm for this framework to address the different training demands of agent and environment. Experiments on instance segmentation and human pose estimation demonstrate the effectiveness of the proposed framework.

/pdf/environment-upgrade-reinforcement-learning-for-non-3dwq7i7zah.pdf

Environment Upgrade Reinforcement Learning for Non-differentiable Multi-stage Pipelines

Constructed wetland system is an economic, efficient and environmentally friendly sewage treatment method based on adsorption and retention of pollutants by substrates, absorption by plants, and degradation by microorganisms and animals. As the largest component in the constructed wetland, substrate not only plays a huge role in the process of decontamination, but also is the hub of other components in the constructed wetland. Therefore, the selection of substrate should be paid special attention to. There are too many types of substrates. In current practical projects, certain substrate is often used directly through previous experience and personal subjective judgment, which is not targeted, resulting in poor treatment results. However, there is a lack of method for comprehensive evaluation and optimization of different types of substrates. This paper reviews the physical and chemical properties of varies constructed wetland substrates in recent years and their removal of major pollutants in sewage. The capacities for the removal of different pollutants by each substrate are evaluated. In view of the situation that it is difficult for a single substrate to achieve the ideal treatment effect, the commonly used optimization measures for constructed wetland substrates are further summarized, and suggestions for optimization methods (i.e., combination and modification) are put forward. This paper aims to make people pay more attention to the importance of substrate for treatment effect and constructed wetlands, and provides some references for how to reasonably select substrate and arrange substrate for different influent water quality. • The physical and chemical properties of varies CW substrates are reviewed. • The capacity for the removal of different pollutants by substrates is evaluated. • The common optimization measures of CW substrates are summarized. • This paper provides references for the selection and optimization of CW substrate. 

Selection and optimization of the substrate in constructed wetland: A review

Zeolite/ZnAl-layer double hydroxides with different Zn/Al ratios and intercalated anions as the substrate of constructed wetlands: synthesis, characterization and purification effect of Hexavalent chromium

Shuqin Xie

Papers

RMPE: Regional Multi-person Pose Estimation

RMPE: Regional Multi-person Pose Estimation

Environment Upgrade Reinforcement Learning for Non-differentiable Multi-stage Pipelines

Selection and optimization of the substrate in constructed wetland: A review

Zeolite/ZnAl-layer double hydroxides with different Zn/Al ratios and intercalated anions as the substrate of constructed wetlands: synthesis, characterization and purification effect of Hexavalent chromium