With computer vision technologies, machines can now scan and annotate complex images, and videos like the human eye and brain can do in a split second. When machine learning models are used for image and video annotation, Human Pose Estimation (HPE) is an effective technique. We’ll dig deeply into human pose estimation in this article. We will determine its operating principle and capabilities to comprehend appropriate business cases. Also, we’ll examine many methods for Human Pose Estimation as a machine learning technology and attempt to categorize the uses for each.
What is pose estimation?
Pose estimation has been widely used in the field of computer vision. Using computer vision techniques to identify and track the movement of a person or an object at a certain time is very useful in various industries. In today’s world where advanced technologies are growing, pose estimation can become an effective and practical tool in sports biomechanics, animation, games, robotics, medical rehabilitation and monitoring.
In fact, pose estimation is done based on the person’s body and the position of the joints in an image or video. For example, it is possible to automatically detect the joints, arms, hips, and spine during sports. Now some people may not know how this can be useful. Suppose an athlete is rehabilitating after an injury or doing strength training. Pose estimation in this situation can help sports analysts to analyze the critical points from the start position to the end of movements. Therefore, analysts can correct postures and help prevent training injuries.
Essentials of Pose Estimation
Pose estimation is used for various tasks such as recognizing human faces or objects from images and videos. Therefore, it should be noted that there are different categories for pose estimation.
When working with humans, analysis of the situation is done by determining different joints of the body, for example, it can be determined by the way a person’s elbow is positioned or the location of his knee joint. This form of pose detection is included in the category of human pose estimation. Input pose estimation models are presented in well-processed images or videos. The model provides an output about key-points with an identifier and a confidence score that determines the probability that a key-point exists at a particular position from the given input.
2D and 3D aspects of pose estimation
Pose estimation can be done in two-dimensional and three-dimensional ways. Maybe some people, after hearing this, associate two-dimensional and three-dimensional concepts to the field of animation. However, the two-dimensional aspects of pose estimation are related to the prediction of key points from images based on pixel values. In order to give acceptable key points of the human body, the majority of 2D human pose estimation algorithms incorporate feature extraction techniques.
In 3D pose estimation, a person or object’s location is predicted using photographs and videos. With the advent of deep learning, these models have improved their performance significantly, but working with them has become more complicated because the datasets must be adjusted with the three-dimensional structural details appropriate to the human body, such as background and lighting conditions. There are also new approaches for single and multi-state estimation, which are related to identifying a person or object or tracking multiple people and objects, respectively.
Importance of pose estimation
In the traditional recognition of objects, people were only understood as a bounding box. By performing posture detection and posture tracking, computers can develop an understanding of human body language. Conventional state tracking methods are not as fast and robust as needed, so blockages are not sustainable. Some of the largest trends in computer vision are being driven by high-performance real-time pose detection and tracking. For instance, real-time tracking of human poses enables computers to grasp human behavior more precisely and naturally. This issue has a great impact in various fields, for example, automatic driving, sports, health care and other cases. Currently, most self-driving car accidents are caused by robotic driving, that is, the self-driving vehicle makes a permitted but unexpected stop and a human-driver crashes into the self-driving car. By tracking and recognizing human poses in real time, computers can better understand and predict pedestrian behavior.
What is human pose estimation?
A computer vision task, “Human Pose Estimation” (HPE), aims to locate a human body in a given scene. Most HPE techniques rely on taking an RGB image with an optical sensor to identify body components and the overall stance. This can be combined with other computer vision technology for fitness and rehabilitation, augmented reality applications, and surveillance. Finding the locations of a human’s limbs, joints, or even face is the core function of the device. A 2D or 3D depiction of a human body model is created using these crucial details.
These models represent a map of the body parts we monitor throughout the movement. This is done for a computer to distinguish between someone simply sitting and someone squatting and determine the flexion angle in a particular joint and whether the movement is carried out correctly. Human estimation pose models can be classified into three categories: skeleton-based, contour-based, and volume-based. Due to its adaptability, the skeleton-based model is the one that is most frequently employed in human pose estimation.
3D modeling of the human body
In human pose estimation, visual input data is used to determine the location of human body parts and display the human body such as the pose of the body skeleton. As a result, human body modeling is an important aspect of human pose estimation. It is used to display features and key points extracted from visual input data. A model-based approach is usually used to describe and infer human body gestures and provide two-dimensional or three-dimensional states. Most of the methods use an N-joints rigid kinematic model, in which the human body is shown as an entity with joints and organs, which has a kinematic structure without information and body shape. There are three types of modeling for human body: the kinematic model, which is also called skeleton-based, is used for 2D and 3D pose estimation. This flexible and intuitive human body model includes a set of joint positions and limb orientations to represent the structure of the human body.
As a result, skeleton pose estimation models are limited in displaying texture or shape information. The sweep model or contour-based model is used for two-dimensional pose estimation. Planar models are used to show the appearance and shape of the body. Usually, in this model, different parts of the body are shown with many rectangles that are close to the lines of the human body. Volumetric model is another model that is used for 3D pose estimation. There are various 3D human body models that are used to estimate the 3D human pose based on deep learning to retrieve the 3D human network.
Recent Advances in Pose Estimation
Here is an overview to the most recent advances in pose estimation field.
Multi-Person Pose Estimation
Scalably detecting pose for multiple people even under occlusion and truncation remains challenging. Object detectors that localize person instances, followed by single-person pose estimation per instance, provide a robust paradigm for crowded multi-person scenes.
Video Pose Estimation
Tracking pose across video frames using recurrent networks exploits temporal coherence for smoothing pose sequences and recovering missing joints. Such sequencing also enables activity recognition from pose dynamics.
Applying pose estimators to new datasets shifts data distributions, hurting accuracy. Domain adaptation techniques adapt models to new target domains using small amounts of labelled target data. This improves generalization.
Pretraining networks using proxy self-supervised tasks on unlabeled data provides useful representations for downstream pose estimation, reducing reliance on large labelled datasets. Tasks like rotated image classification prompt implicit pose learning.
Allowing human annotators to iteratively correct predicted joint positions and retrain models combines automation with human feedback for highly precise pose annotation. This addresses corner cases.
Synthetic Data Generation
Blending motion-captured CG models onto real backgrounds generates realistic synthetic training data with perfect joint annotations. This expands limited real datasets cost-effectively.
The main challenges of pose estimation
Estimating the human pose is a challenging task because the appearance of the body changes dynamically due to the variety of clothes, viewing angles, and different backgrounds. Pose estimation should be robust to challenging real-world variations such as light, water, and weather. As a result, it is challenging to identify fine-grained joint coordinates for image processing models. Especially the traces of small joints that are hard to see.
Estimation of head position
Estimating the position of a person’s head is one of the common problems in the field of computer vision. The head state estimation process has various uses, such as helping to estimate gaze, modeling attention, adapting 3D models for video, and performing face alignment. Traditionally, the head posture is calculated using key points of the target face and by solving the problem of matching the two-dimensional to three-dimensional pose with an average human head model. The ability to retrieve the 3D head pose is a byproduct of key point-based face pose analysis, which is based on extracting 2D face key points with deep learning methods. These methods are resistant to occlusion and severe changes of pose.
Estimation of the condition of animals
Most of the advanced methods are focused on the detection and tracking of human body pose. However, some models are for use with animals and cars. Animal pose estimation is associated with other challenges such as limited labeled data that require manual image collection and annotation and many self-obstacles. As a result, data sets for animals are usually small and include limited animal species. When working with limited available data and small datasets, active learning and data augmentation provide useful methods. Both techniques greatly contribute to more effective training of vision algorithms and reduce annotation work for training custom AI models.
Due to the frequent interactions that result in occlusion and the difficulty of associating detected key points with the appropriate individual, estimating the pose of numerous animals is a difficult computer vision challenge. It is also challenging to have animals with a very similar appearance that are more closely related to humans. To address these issues, transfer learning techniques have been developed to re-apply methods from humans to animals.
Human AI pose estimations fitness product development challenges
The most common challenges we face in the development of a fitness product that requires human ai pose estimations are either related to low data quality or the complexity of the work.
- Diversity in poses: Even in the field of a sport, there are different poses that a model should recognize. When we consider body shape, differences and clothes, this number will increase. To overcome this challenge, we need to collect high-quality data and use post-processing to increase the accuracy of key-point tracking.
- Occlusion Control: In real-world scenarios, body parts can be partially or completely occluded by objects or other body parts. The program must be able to handle these blockages and provide accurate estimates of the situation.
- Multi-person ai pose estimations: In scenarios where there are many people, accurate ai pose estimations for all people in the frame can pose challenges, especially when those people are interacting with each other or blocking each other.
- Model complexity and size: More advanced high-precision models may be computationally expensive, but they have a lot of memory. Balancing model complexity and performance is a challenge for deployment on different devices.
- Limited data and annotated datasets: Learning situation estimation models require large and diverse annotated datasets. For very specific movements that may occur in the rehabilitation and sports industry, custom data is needed to train the model.
Despite all these challenges, here is a list of things you should manage when it comes to data:
- Difference in frame rate from sample to sample
- Poor light conditions
- Inappropriate camera angles
- Artifacts such as the rolling sphere effect, color bending, display changes, and more
- Low resolution videos
- A user who wears clothes that interfere with the detection of key points, such as dresses, robes or large clothes
- A user who wears clothes that make him blend in with the background
Unlike other computer vision techniques, privacy concerns are not really relevant in human gesture estimation projects because we can only transmit and store key information, even in cases where the model detects the user’s face and head movements. , this data is anonymous because we extract the coordinates from the image and manipulate them to reach the pose estimation results, rather than storing personal data like face recognition works; Of course, these details depend on the request and project requirements.
How to train a human pose estimation model?
As a machine learning technology, human pose estimation requires data to be trained. Neural networks are employed as the foundation for human pose estimation because it handles the challenging task of locating and identifying many objects on the screen. Using readily available datasets like HumanEva, COCO, MPI Human Pose, and Human 3.6M is the most effective method for training neural networks because doing so demands vast data. With human pose estimation, most of these datasets are appropriate for fitness and rehabilitation applications. However, in terms of more unique motions or specific jobs like surveillance or multi-person posture assessment, these do not provide high accuracy. The remaining scenarios will require data gathering since a neural network will need high-quality samples for precise item detection and tracking. Experienced data science and machine learning teams can be beneficial in this situation because they can offer advice on data collection strategies and take care of the actual model construction.
Emerging Applications of Pose Estimation
In this section you can see what pose estimation is capable of:
Analyzing athlete pose and motion patterns enables automated coaching feedback, technique improvement and injury risk assessment. Startups like PlaySight are commercializing sports computer vision.
Sign Language Recognition
Modeling hand and body pose dynamics as people sign enables new ways to translate sign language into text or speech. This expands accessibility for signers.
Animation and Motion Capture
Estimating 3D pose from video provides low-cost motion capture for animating digital avatars and CGI models for films, games and virtual worlds.
Inferring coarse demographic attributes like age and gender from customers’ pose data reveals in-store traffic patterns. This guides marketing and merchandising strategies.
Elder Care and Rehabilitation
Tracking pose can quantify progress during physical therapy. It also enables monitoring elderly patients at home for fall detection, gait analysis and safety.
Autonomous Vehicles and Robots
Understanding human pose enables safer navigation and interaction for autonomous vehicles, delivery robots and collaborative industrial robots.
What Are the Most Popular Machine Learning Models for Human Pose Estimation?
For human pose estimation data sources, dozens of machine learning (ML) algorithmic models have been created. Before the invention of these techniques, human pose estimation could only identify a human’s location inside a video or image. Accurate estimation and annotation of human body expression and movement have required the development of algorithmic models, computer capacity, and AI-based software applications. The good news is that the most powerful and user-friendly machine learning or artificial intelligence (AI) technologies work with any algorithmic model. These models can be used with an AI-based tool to annotate and assess photos and videos of human posture estimates.
A trainable framework for end-to-end multi-person posture estimation that only requires one step. Using a design that uses multiple-scale feature representations, it employs an innovative waterfall methodology to maximize reliability while minimizing the need for post-processing.
The Google-developed and endorsed open-source “cross-platform, customizable ML solution for live and streaming media” is called MediaPipe. MediaPipe is created for face detection, hand tracking, pose validation, real-time eye tracking, and other general applications. Via the Google AI and Google Developers Blogs, Google offers a wealth of in-depth use examples, and multiple MediaPipe Meetups took place in 2019 and 2020.
For real-time multi-person tracking, estimation, and annotating, OpenPose is a well-liked bottom-up machine learning as a service approach. This open-source technique works well for locating key points on the body, hands, feet, and faces. An API called OpenPose has a lightweight version that works well with Edge devices and interacts easily with various CCTV cameras and systems.
A unique approach called Residual Steps Network (RSN) “proficiently combines data that has an identical spatial scale (intra-level features) to create sensitive localized models, which maintain rich low-level spatial information and lead to exact keypoint localization”. This method uses a Pose Refine Machine (PRM) to manage the trade-off between “local and global representations in output features”, improving keypoint characteristics. With cutting-edge performance against the COCO and MPII benchmarks, RSN won the 2019 COCO Keypoint Challenge.
DARKPose or Distribution-Aware coordinate Representation of Keypoint (DARK) Pose is a cutting-edge method to outperform conventional heatmaps. DARKPose uses “a more systematic distribution-aware decoding approach” to decode “expected heatmaps into the final joint coordinates in the original image space”. The results of the human pose estimation model are improved by the production of more precise heatmap patterns.
Classical vs Deep Learning-based approaches
The term “classical approaches” typically refers to strategies and procedures incorporating algorithms for machine learning. For instance, random forests within a “pictographic structure framework” was used in earlier work to assess human pose. Joints in the human body were predicted using this method. One of the conventional approaches to estimating human position is the pictorial structural framework (PSF). PSF was made up of two parts:
It simulates the probability that a specific component will be present at a specific location. To put it another way, it names the physical components.
The process is known as modelling the probability distribution over pose using the discriminator’s output; the modelled posture must be accurate. The PSF objective presents the human body as coordinates for each body part in a given input image. PSF employs nonlinear joint regressors, preferably a two-layered regressor from a random forest.
These models perform well when the input image contains distinct and apparent limbs. Still, they cannot recognize, and model limbs concealed or obscured from a particular perspective. These problems were solved using feature-building techniques such as histogram-oriented gaussian (HOG), contours, histograms, etc. Despite employing these techniques, the classical model lacked accuracy, correlation, and generalization skills; therefore, switching to a more effective strategy was only a matter of time.
Human Pose Estimation using Deep Neural Networks
Deep neural networks are commonly employed for human pose estimation because of their ability to learn complicated features from large quantities of data and adapt effectively to new ones. Convolutional neural networks (CNNs) are a common form of deep neural networks that perform image processing applications such as human pose estimation. Deep neural networks may be used to estimate human poses in various ways. A fully convolutional network (FCN) is a common approach that takes a whole image as input and generates a histogram of the probability distribution of each key point. Another method employs a multi-stage network that continuously refines the hierarchical significant point estimate. A large dataset of labelled images with the position of each key point marked is commonly used to train deep neural networks for human pose estimation. The network is trained to minimize the difference between the predicted and ground truth key points using an appropriate loss function, such as the mean squared error (MSE) or the smooth L1 loss. Deep neural network-based human pose estimation has several practical applications, including human-computer interaction, activity detection, and motion capture in sports and entertainment.
Common Mistakes When Doing Human Pose Estimation
Using tools and algorithms for human pose estimation on animals can result in some of the most significant errors. Animals naturally move very differently from humans, except for those with which we share the most DNA, such as giant primates. Yet, employing the incorrect tools is another more typical error. Regardless of the machine learning model employed, using the incorrect tool could result in losing days or even weeks of annotation. An annotation team may lose a lot of money if there are a few frame synchronization issues or if they have to split a large video into smaller ones.
Human Pose Estimation Model Evaluation Metrics
The artificial intelligence pipeline includes evaluation metrics. They are employed to evaluate how well machine learning models work and ensure that advancements are achieved. A few standard metrics for categorization jobs are recall, precision, and accuracy. Measures like the Root Mean Squared Error and R-Squared are used to assess regression algorithms. In this part, we’ll look at a few metrics for measuring how well human posture estimation models perform.
Percentage of Correct Parts (PCP)
PCP is a metric that, as its name implies, tells us whether or not we have correctly characterized a limb. The difference between the projected key points and the actual ones must be less than half the length of the limb for it to be considered a valid part.
However, this metric has a limitation because human bodies have a range of limb lengths. Because shorter limbs are penalized more harshly than longer ones, this measure can produce findings that differ significantly between datasets.
Percentage of Correct Key-points (PCK)
PCK can be utilized to get around PCP’s problem of punishing shorter limbs. This metric will only accept a key point as correct if the difference between the true and anticipated points falls within a predetermined threshold. The threshold for PCK could be set at 0.2. As a result, the gap between valid and anticipated points must be smaller than 0.2 times the link between a person’s skull and feet. Since persons with smaller skull bone linkages will have smaller limbs and vice versa, this will vary from person to person and is a more dependable way to determine the correct spots.
Percentage of Detected Joints (PDJ)
Another statistic that lessens the issue of short limbs is PDJ. The genuine joint must be within a fraction of the anticipated joint’s diameter for this metric to deem a key point correct. Again, since the fraction will differ between individuals, this method is more logical than PCP.
Object Keypoint Similarity (OKS) based mAP
OKS determines the separation between the actual and anticipated joints. The scale of the person then normalizes this value. Adding the critical points of all the samples determines this measurement for each individual in the frame.