It is challenging to develop programs that comprehend their environments. A developer must first choose and design applicable machine learning models and algorithms, then build prototypes and demos, balance resource usage with solution quality, and ultimately determine and decrease.
Frameworks and libraries address all of these challenges. A developer can utilize the OpenPose vs MediaPipe frameworks to create prototypes that integrate existing perception components for usage in cross-platform applications and assess system performance and resource consumption on the target platforms. This article will examine the difference between OpenPose vs MediaPipe and their functions. We’ll go through the differences between them.
What is OpenPose?
Input data for some computer vision applications must include 2D human pose predictions. As follow-up duties, this also incorporates image recognition in the image editing field and AI-powered video analytics. This necessitates further research in image recognition and AI-based video analytics. Pose estimates for individuals and groups is a crucial computer vision task with applications in various fields, including action identification, security, sports, and more.
For the first time, the human body, foot, hand, and facial key points on single pictures have been effectively identified by OpenPose, a real-time multi-person human pose identification library developed by Carnegie Mellon University researchers. It is the cutting-edge approach for real-time human posture estimation. The open-sourced code base is very well documented and available on GitHub. To implement the neural network, the OpenPose writers use Caffe. If you are unfamiliar with Caffe, don’t panic; like many other deep learning frameworks, Caffe is quite simple to use.
What are the features of OpenPose?
The following are some of the most noteworthy aspects of the OpenPose human posture detection library, while there are many others:
- 3D single-person keypoint detection in real-time
- 2D multi-person keypoint detections in real-time
- Single-person tracking to speed up recognition and smooth out the visuals
- Extrinsic, intrinsic, and distortion camera parameters are estimated using a calibration toolkit.
How Does OpenPose Work?
Over the years, various methods have been proposed for examining human positions. Being aware of someone’s perspective opens the door to various practical uses. The first few layers are used by the OpenPose package to extract features from a picture. The parts are then sent into two parallel layer divisions of convolutional networks. The first division predicts a set of 18 confidence maps, each corresponding to a unique portion of the human stance skeleton. The following branch predicts an additional 38 Part Affinity Fields (PAFs), which show the level of relationship between parts.
Further steps involve cleaning up the branch-provided estimates. Confidence intervals are used to generate bipartite graphs between pairs of components. Confidence maps are used to build bipartite graphs between pairs of components. Weaker linkages are removed from bipartite graphs using PAF values.
What are the limitations of OpenPose?
The fundamental problem with OpenPose is that its outputs are low-resolution, which limits the level of detail in keypoint estimates. As a result, OpenPose is less suitable for applications that require high levels of precision in the assessment of movement kinematics, such as elite sports and medical evaluations. Additionally, OpenPose is recognized as being extremely inefficient since every inference costs 160 billion floating-point operations (GFLOPs). Despite these problems, OpenPose is still a popular network for single-person HPE that performs markerless motion capture.
What is MediaPipe?
MediaPipe is a cross-platform pipeline framework for developing unique machine-learning solutions utilized by Google internally in several products and services. It was initially created to analyze YouTube videos and audio in real time. This framework, open-sourced by Google, is currently in the alpha stage and covers Android, iOS, and embedded devices like the Raspberry Pi and Jetson Nano.
MediaPipe was developed for machine learning as a service(MLaas) teams, software engineers, students, and researchers who publish code and prototypes as part of their research projects.
The MediaPipe framework is primarily used to quickly develop vision pipelines that include reusable parts and AI models for inferencing. Also, it makes it easier to integrate computer vision software into applications and demos running on many hardware platforms. Teams can incrementally develop computer vision pipelines using the configuration language and evaluation tools.
What is MediaPipe used for?
MediaPipe is divided into three primary parts:
- A framework for inference from sensory input
- A set of tools for performance evaluation
- A library of reusable inference and processing components.
A developer can gradually prototype a pipeline using MediaPipe. A vision pipeline is described as a directed graph of components, where each component is a node (“Calculator”). Data “Streams” connect the calculators in the graph. Each stream represents a time series of data “Packets”. The calculators and streams collectively define a data-flow graph. The time stamps of the packets that traverse the graph are used to group them. Each input stream maintains its queue to enable the receiving node to consume the packets at its rate. Wherever in the graph, calculators can be inserted or removed to improve the process gradually. Moreover, developers can create unique calculators.
These are particular C++ computing units that have been given tasks to complete. Data packets (such as video frames or audio segments) enter and exit a calculator through its ports. Calculators indicate the type of packet payload that will travel through the port when initialized. The Framework integrates Open, Process, and Close procedures in the calculators for each graph run. The calculator is started by opening; each time a packet is received, the process is repeated. After a full graph run, the process is terminated.
Think about the first calculator in the graph above as an illustration. The ImageTransform calculator receives an image as input and outputs a transformed version of the input image. The second calculator, ImageToTensor, on the other hand, accepts an image as input and produces a tensor.
Mediapipe: AI models vs. Applications
Generally, neural networks like TensorFlow, PyTorch, CNTK, or MXNet are used to analyze different streams of incoming picture or video data. One input produces one output when using such models to handle data, which enables extremely effective processing execution.
On the other hand, MediaPipe supports more complicated and dynamic behavior and operates at a much higher semantic level. For instance, a single input can result in zero, one, or several outputs, which neural networks cannot model. Compared to batching techniques, streaming processing is needed for AI perception and video processing.
MediaPipe is significantly better suited for processing audio and sensor data because it supports operations on any data type and natively supports streaming time-series data.
The MediaPipe tracer module logs events with several data fields by timing them over the graph (time, packet timestamp, data Id, node Id, stream ID). Also, the tracer provides histograms of different resources, such as the CPU time used by each calculator and stream.
The tracer module can be activated via a configuration parameter and collects timing data as needed (in GraphConfig). The user can fully omit the tracer module code with a compiler flag.
The timing data is used to create reports and visualizations of specific packet flows and calculator executions. The timing data is used to identify several issues, including unanticipated real-time delays, memory buildup as a result of packet buffering, and collating packets at various frame rates.
For performance optimization, the aggregated timing data can be used to provide average and severe latencies. The timing information can also be examined to find the crucial path nodes whose efficiency affects end-to-end latency.
What are the advantages of MediaPipe?
- End-to-end acceleration: ML inference and video processing are built-in quickly using standard hardware, such as GPU, CPU, or TPU.
- Build once and deploy everywhere: The unified framework is ideal for platforms like Android, iOS, desktop, edge, cloud, web, and IoT.
- Pre-packaged solutions: The full potential of the MediaPipe framework is shown via prebuilt ML applications.
- Free and open source: The framework is entirely flexible, expandable, and released under the Apache 2.0 license.
What is the disadvantage of MediaPipe?
The lack of documentation with MediaPipe is a disadvantage. The “documentation” for MediaPipe consists of a website with high-level explanations of its ideas and a very straightforward code example. A developer must explore the MediaPipe example source code to comprehend MediaPipe fully.
What are the differences between OpenPose vs MediaPipe?
There is a main difference between OpenPose vs MediaPipe, and There are a maximum of 17 often found key points among the four HPE libraries. The ears, eyes, and nose are the important parts on the head that are most frequently seen (5 key points). The six usually found key points of the upper body are the shoulders, elbows, and wrists, whereas the six key points of the lower body are the hips, knees, and ankles. To reach the maximum number of key points—135 for OpenPose and 33 for MediaPipe Pose, respectively—more annotations are provided for the key points at the face, hand, and foot.
Further key points from OpenPose include 70 key points for the face, 20 for both hands, 1 for the upper body, and 7 for the lower body. Six additional key points for the head, six for the upper body, and four for the lower body are provided by the MediaPipe Pose. Both top-down and bottom-up techniques can be used for keypoint identification in HPE libraries. The top-down method assigns each individual to a distinct bounding box once the number of persons is initially determined from the input.
The key point estimation is then carried out in each bounding box. In contrast to the top-down approach, the bottom-up approach executes key point detection in the first phase. The next step is to arrange the essential topics according to human instances. PoseNet, MediaPipe Pose, and MoveNet are the two libraries that use the top-down method for estimating human stance, while OpenPose and PoseNet use the bottom-up method. The four HPE libraries each employ a different set of underlying networks for pose estimation. MediaPipe Pose uses the Convolutional Neural Network (CNN), OpenPose uses ImageNet with the VGG-19 backbone, PoseNet uses ResNet and MobileNet, and MoveNet uses MobileNetV2.
Architectural Differences of OpenPose vs MediaPipe?
OpenPose and MediaPipe leverage different architectural approaches for pose estimation:
- OpenPose relies on a bottom-up approach using Part Affinity Fields (PAFs) to detect body part keypoints and assemble them into full body poses. PAFs capture the association between neighboring body parts.
- In contrast, MediaPipe utilizes a top-down approach by first detecting a person instance and then identifying the semantic keypoints. It builds on top of machine learning frameworks like TensorFlow rather than custom architectures.
- OpenPose first generates candidate keypoints through repeated spatial convolutions and then associates them via PAFs. MediaPipe takes a hierarchical approach by first detecting a person and then finding keypoints.
- OpenPose can infer poses in a single forward pass but requires more complex post-processing. MediaPipe simplifies post-processing but requires multiple passes end-to-end.
OpenPose emphasizes bottom-up heatmaps while MediaPipe takes a top-down semantic approach reflecting their origins from academic research versus industry labs respectively.
Ease of Use of OpenPose vs MediaPipe
Both frameworks aim to provide accessible workflows:
- OpenPose offers ready-to-use pretrained models for body, hand and face keypoint detection via simple Python and C++ APIs.
- MediaPipe provides cross-platform SDKs for iOS, Android, web, C++ etc. Integration into apps is straightforward.
- OpenPose has detailed API documentation but lacks tutorials for new users. MediaPipe provides richer documentation and coding examples.
- OpenPose requires compiling from source for some languages which complicates usage. MediaPipe binaries simplify integration.
- MediaPipe has a more streamlined workflow and processing pipeline compared to OpenPose’s elaborate configurations.
Overall, MediaPipe’s unified cross-platform SDKs, extensive documentation and turnkey examples give it a slight edge in usability.
Customizability in OpenPose vs MediaPipe
Both frameworks allow customization but differ in extensibility:
- Retraining OpenPose for new tasks requires modifying network architectures and re-implementing downstream processing. MediaPipe simplifies retraining.
- MediaPipe interoperability with TensorFlow, PyTorch, OpenCV facilitates extending functionality. OpenPose is more self-contained.
- OpenPose allows greater customization of detected keypoints. MediaPipe has pre-defined hand and face landmark models.
- OpenPose relies on fixed networks like VGG and ResNet backbones. MediaPipe enables custom backbones.
- MediaPipe trains models end-to-end allowing joint optimization. OpenPose has discrete training steps.
Overall, MediaPipe’s integration with major frameworks and end-to-end training enables simpler customization compared to OpenPose’s fragmentation.
Human pose estimation using mediapipe pose
Elderly people who live alone are exposed to risks such as falling and getting injured, so they may need a mobile robot that can automatically control and recognize their situation. It is true that deep learning methods are actively evolving in this field, but there are still limitations in the estimation of situations that do not exist in the training data set or are few. For a lightweight approach, an off-the-shelf 2D pose estimation method, a more complex anthropomorphic model, and a fast optimization method to estimate joint angles are combined for 3D pose estimation. The loss function departure of the center of mass from the center of the supporting feet and the penalty functions for the proper joint angle rotation range are unique solutions to the depth ambiguity problem of 3D pose estimation.
Due to advances in medical technology and proper nutrition, the elderly population is constantly increasing. Elderly people are supervised by health managers or caregivers in nursing centers, homes or hospitals. As we said in the above section, the elderly who live at home are exposed to various risks unless a family member, social worker or a caregiver is with them. A mobile robot that moves around the house, takes pictures of the posture of the elderly in appropriate positions and automatically analyzes their posture or current activity to warn the relevant people in case of a dangerous situation or problem. For this reason, the whole body joint angle data for daily life activities is very useful information to identify, transfer to server and restore to DB as historical data.
Human gesture estimation technology is actively being researched around the world in various fields such as sports, work monitoring, elderly care at home, education at home, entertainment, movement control, and other cases. In general, human pose estimation is classified into two-dimensional and three-dimensional coordinate estimation methods, single-user and multi-user methods based on the number of target subjects, methods based on monocular image and multi-view image based on the number of subjects. Exclusively, according to the structure of the deep learning process, human gesture estimation is classified into single-stage methods and two-stage methods. One-step methods that directly map input images to 3D body joint coordinates can be classified into two categories: Detection-based methods and Rgression-based methods.
Detection-based methods predict a probability heat map for each joint, the location of which is determined by considering the maximum likelihood of the heat map, while regression-based methods directly determine the location of the joints relative to the root junction or They estimate the angle.