ProjectBlog: What You Need to Know About Pose Estimation?

Blog: What You Need to Know About Pose Estimation?

One of the most sought after aspects of computer technology has been to understand human appearance using images and videos. The description of human poses interpreted as a combination of smaller and fundamental aspects including posture, gesture, and outlook among other factors. The demand for an in-depth study into human pose has been fueled by technologies that analyze human beings and their interaction with their surroundings. It has led to two distinct issues including human pose estimation and deeper understanding depending on the evaluation of the pose.

Pose Estimation and representations in 3D space.

The pose estimation refers to a computer vision technique, which can detect human figures in both images and videos. It helps determine whether a person’s elbow is showing in the picture. The technology does not recognize who is in the view, but the algorithm works only to detect the critical body joints.

How Does Pose Estimation Work?

The performance of a pose estimation program depends on the device and its output stride including heat maps and offset vectors. The PoseNet model is size invariant. It can easily predict the pose position on the same scale as the one in the image, whether the image size is reduced or not. One can configure the PoseNet to have increased accuracy, but it comes at the expense of performance.

The output stride of the pose estimation process considers how much the person is decreasing the output to the input image size. It will affect the size of the layer and the model output. In short, the higher the output stride, the smaller the resolution of the sheet in the network and production, which corresponds their accuracy.

What are the Issues Faced By Pose Estimation?

The human pose estimation is a significant issue in regards to computer vision and studied for more than 15 years. Human pose estimation permits a higher level of reasoning in human-computer interaction and activity recognition. It is an essential building block for markerless motion capture (MoCap) technology. The MoCap technology assists with different applications ranging from character animation to clinical analysis of gait technologies.

I Want to Know About the Application Areas of the Pose Estimation.

Apart from learning to understand different human poses, pose estimation technology also offers the benefits below:

1. Assisted Living

With the advent in artificial intelligence and robotics, there is no denying the fact that robots are the future. Personal care robots would need to understand human detection including pose estimation to perform various tasks.

2. Character Animation

Until present, character animation has been a manual process. With pose estimation, poses can be directly synced to a real-life actor using specialized systems. The older systems relied on markers or special suits. The recent advancement allows for motion capture and enabled markerless applications.

3. Video Games

Pose estimation has been most useful for the video gaming industry. The technology has become popular after Microsoft’s Kinect sensor. The Kinect can track the user to render their in-game avatar, apart from gesture recognition to help the user interact with the game. In the gaming industry, pose estimation has a real-time requirement.

Pose estimation using for motion capture for gaming.

4. Intelligent Driver Assist System

Around the world, car accidents account for two percents of death. The intelligent driver assist system in future cars would utilize pose estimation for any emergency alert. These cars would also include pedestrian detection algorithm to make smarter decisions on the road.

5. Medical Applications

The technology of pose estimation is useful for detecting any medical issues such as scoliosis by analyzing any abnormality in the patient’s posture. It also takes into consideration physical therapy and brain development in children by monitoring motor skills functions.

6. Other Areas

Besides these, the different application areas of pose estimation include video surveillance, behavior understanding, sign language detection, markerless motion capturing, human-computer interaction, and animal tracking.

What is Wrnch AI?

The Wrnch AI is deep learning software developed by Dr. Paul Kruszewski, an AI entrepreneur based in Canada. It can easily integrate into different applications including augmented reality, virtual reality, human-robot interaction, and markerless motion capture. Apart from this, the Wrnch has enormous application capacity, right from making the world a safe place to providing a healthier living to people. In short, this AI will revolutionize the present world and bring a positive difference in the everyday lives of ordinary people, however, this technology does not come cheap.

I Want to Know About the Frameworks Used for Pose Estimation.

The human pose estimation has been an essential aspect of study in the field of computer vision. However, no one can deny the massive possibilities it represents including video surveillance, advanced driver assistance systems, and sports analysis. In short, it is the prediction of the movement of a body part or joint positions of a person from an image or video. Currently the most popular frameworks for pose estimation are OpenPose, Wrnch AI and DensePose. Let’s quickly glance at these frameworks.


OpenPose is a library for real-time multi-person keypoint detection and multi-threading written using C++ but also works in Python. It utilizes OpenCV and Caffe. OpenPose represents the real-time system to jointly detect the human body and hand key point on a single image. The system computational performance on body keypoint estimation is invariant to the number of identified persons in an image. It is the first type of framework utilized for pose estimation.

What is the Difference between Wrnch and OpenPose?

The Fundamental Difference

As far as the difference between the Wrnch AI and OpenPose goes, there are three fundamental differences between the two:

1. The WrnchAI and OpenPose are similar in accuracy. However, the WrnchAI is amazingly fast in performance.

2. OpenPose licensing prevents its use in sports applications. On the contrary, WrnchAI does not have this issue.

3. The OpenPose is an opensource library whereas WrnchAI requires a commecial license which is out of budget for hobbyists and students.


The DensePose refers to Facebook’s real-time approach for mapping all individual pixels of 2D RGB images into a 3D surface-based model of the human body. Recent research into human understanding aimed at localizing a sparse set of joints including wrist, the elbow of a human. It would let applications perform gesture or action recognition, but also provide reduced image interpretation. The DensePose project aims to understand humans in images in terms of surface-based models.

Densepose Mesh Pose Estimation.


The state-of-the-art methods utilized for human movement detection and pose estimation needs training samples for the best performance. Although an extensive collection of the database exists, it is often not enough owing to uncontrolled shape and poses. We need to develop advanced computer graphics to sample the realistic appearance of the human being.

To Learn how to implement Pose Estimation using OpenPose, Check out the course by Augmented Startups — Click Here

Source: Artificial Intelligence on Medium

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