The following is part of my results. My endogenous variable is a categorical variable, including 7 types. I show the results of FE and GMM with different numbers of instruments. In the 6th type (second to last row), the coefficient of FE is 0.0928, while in GMM, such coefficient becomes 0.2060 or lager.Timberlake (Portugal) and FEP (U.
The course will take place at FEP (U. Porto) on January 2018.This video simplifies the understanding of generalised method of moments (GMM) technique in such a manner that beginners can comprehend. The video series wil.All participants are invited to wine tasting and special dinner on thursday, 25th. The aim of these courses is to familiarize the participants with key econometric tools commonly used in applied research.31 answers. Dear experts, I am using STATA command xtabond2 and system GMM for my very first project. According to ivreg gmm q demandshiftrs (p supplyshiftrs ) with heteroskedasticit, y the GMM estimator will be more e cient than the 2SLS estimator.
Right picture ： Rendered human pose skeleton. Left ： Human posture skeleton COCO Key format. Ricardo Mora GMM estimation.
Multi person posture estimation is more difficult than single person situation , Because the position and number of people in the image are unknown. Natural , These methods are not particularly useful in many real-life scenes where images contain many people. These methods usually identify each part first , Then form a connection between them to create a pose. Earliest （ And the slowest ） The method is usually to estimate the pose of a single person in an image with only one person.
Bottom ： A typical bottom-up approach. Top ： A typical top-down approach. This approach is called the bottom-up approach. Another method is to detect all parts of the image （ That is, everyone's part ）, And then connect / Groups belong to different people. This approach is called the top-down approach.
This is shown below OpenPose The architecture of the model. Like many bottom-up approaches ,OpenPose First, detect the part belonging to everyone in the image （ Key points ）, Then assign the part to different individuals. The paper ：OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields(2019)OpenPose It is one of the most popular bottom-up human posture estimation methods , Part of the reason is that they are well documented GitHub Implementation code. In the next section , We will review some popular top-down and bottom-up approaches. In this paper , We will focus on multi person pose estimation using deep learning technology. It is difficult to determine which method has better overall performance , Because it actually boils down to people detectors and associations / Which of the grouping algorithms is better.
Use PAF value , You can trim weak links in bipartite graphs. Use the component confidence graph , A bipartite diagram is formed between component pairs （ As shown in the figure above ）. The subsequent stage is used to refine the prediction made by each branch.
Select a subset of body parts from the set of candidate body parts. This set represents all possible positions of each person's body part in the image. The author accomplishes this task by defining the following questions ：Generate a set D Body part candidate. The paper ：DeepCut: Joint Subset Partition and Labeling for Multi Person Pose EstimationDeepCut It is a bottom-up multi person human posture estimation method.
Body part candidates are through Faster RCNN or Dense CNN To obtain the. Consider candidate sets from body parts D Two body part candidates d and d' And from the category C Categories c and c'. The body part class represents the type of part , for example “ Arms ”、“ leg ”、“ trunk ” etc.Divide the body parts belonging to the same person.
Obviously , The above statement can be expressed by a linear equation as (x,y,z) Function of. The last statement can be used to divide gestures belonging to different people. If the above value is 1, Indicates the candidate body part d Belong to c class , Candidate body parts d' Belong to the category c', The last candidate body part d,d' Belong to the same person. They also defined z(d,d',c,c') = x(d,c) * x(d',c') * y(d,d'). If x(d,c) = 1, Indicates the candidate body part d Belong to category c.Besides ,y(d,d') = 1 Represents a candidate body part d and d' Belong to the same person.
Therefore , Errors in positioning and repeating bounding box prediction will lead to poor performance of pose extraction algorithm. The author believes that the top-down method usually depends on the accuracy of the person detector , Because pose estimation is performed on the area where the character is located. The paper ：RMPE: Regional Multi-person Pose EstimationRMPE It is a popular top-down attitude estimation method. For exact equations and more detailed analysis , Please check their papers by yourself.
RMPE The remarkable feature of this technology is that it can be extended to personnel detection algorithms and SPPE Any combination of. Besides , The author introduces a pose guidance suggestion generator (Pose Guided Proposals Generator) To increase the training sample , So as to better help train SPPE and SSTN The Internet. Last , Use parametric attitude non maximum suppression (NMS) Technology to deal with the redundant attitude deduction problem. Space De-Transformer The Internet (SDTN) It is used to remap the estimated human posture back to the original image coordinate system. In this extracted region, a single person pose estimator is used (SPPE) To estimate the person's posture skeleton. To solve this problem , The author proposes to use symmetric space transformer The Internet (Symmetric Spatial Transformer Network, SSTN) Extract high-quality single person areas from inaccurate bounding boxes.
Regional proposal network (Region Proposal Network, RPN) Use these feature maps to get the bounding box candidates of existing objects. The basic architecture first uses CNN Extract feature map from image. Describe Mask RCNN The flow chart of the architecture. The basic architecture can be easily extended to human pose estimation. The model predicts the bounding box position of various targets in the image and the mask of semantic segmentation targets at the same time.
Let's focus on the branch that performs segmentation. Now? , This extracted feature is passed to CNN Parallel branch of , Final prediction for bounding box and segmentation mask. Because the candidate bounding box can be of various sizes , Therefore, the use is called RoIAlign To reduce the size of the extracted features , Make them all have a uniform size.
Meanwhile , The target detection algorithm can be trained to identify the position of personnel. We can extract the key points belonging to each person in the image by modeling each type of key points as a different class and treating it as a segmentation problem. Split branch output K Size is m x m Binary mask for , Each binary mask represents all targets belonging to this class.
Activity identificationTracking the change of a person's posture over a period of time can also be used for activities 、 Gesture and gait recognition. Paperwithcode A list of papers on multiplayer pose estimation ：Attitude estimation has been applied in numerous fields , Some of these areas are listed below. For a more detailed list of methods , You can view the following links ：2.
Great progress has been made in the field of human pose estimation , This enables us to better serve countless possible applications. （ for example ： Airport runway signal 、 Traffic police signal, etc ）.Applications that can enhance security and monitoring. Applications that can understand whole body sign language. You can teach yourself the correct way of exercise 、 Applications for motor skills and dance activities.
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