Real-time 3D Human Pose Estimation based on kinematic model

1. 3D human pose estimation is used to predict the locations of body joints in 3D space. Besides the 3D pose, some methods also recover 3D human mesh from images or videos. This field has attracted much interest in recent years since it is used to provide extensive 3D structure information related to the human body. It can be applied to various applications, such as 3D animation industries, virtual or augmented reality, and 3D action prediction. 3D human pose analysis can be performed on monocular images or videos (normal camera feeds). Using multiple viewpoints or additional sensors (IMU or LiDAR), 3D pose estimation can be applied with information fusion techniques, which is a very challenging task. While 2D human datasets can be easily obtained, collecting accurate 3D pose image annotation is time-consuming, and manual labeling is not practical and expensive. Therefore, although 3D pose tracking has made significant advancements in recent years, especially due to the progress made in 2D human pose estimation, there are still several challenges to overcome: Model generalization, robustness to occlusion, and computation efficiency.
2. 3D Human Body Modeling In human pose estimation, the location of human body parts is used to build a human body representation (such as a body skeleton pose) from visual input data. Therefore, human body modeling is an important aspect of human pose estimation. It is used to represent features and key points extracted from visual input data. Typically, a model-based approach is used to describe and infer human body poses and render 2D or 3D poses.
3. Most methods use an N-joints rigid kinematic model where a human body is represented as an entity with joints and limbs, containing body kinematic structure and body shape information. Kinematic Model, also called the skeleton-based model, 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 human body structure. Therefore, skeleton pose estimation models are used to capture the relations between different body parts. However, kinematic models are limited in representing texture or shape information.

Real-time Hand Gesture Recognition/Sign Language Recognition based on Machine Learning models for Human Computer Interaction                           

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time.

Sign Language is a form of communication used primarily by people hard of hearing or deaf. This type of gesture-based language allows people to convey ideas and thoughts easily overcoming the barriers caused by difficulties from hearing issues.

A major issue with this convenient form of communication is the lack of knowledge of the language for the vast majority of the global population. Just as any other language, learning Sign Language takes much time and effort, discouraging to from being learned by the larger population.

However, an evident solution to this issue is present in the world of Machine Learning and Image Detection. Implementing predictive model technology to automatically classify Sign Language symbols can be used to create a form of real-time captioning for virtual conferences like Zoom meetings and other such things. This would greatly increase access of such services to those with hearing impairments as it would go hand-in-hand with voice-based captioning, creating a two-way communication system online for people with hearing issues.

Cloud Platform Construction for the Environmental Awareness of Internet of Things (IOT) system

Building upon existing industrial pollution treatment equipment, such as integrated sewage treatment systems, sensors, global positioning systems (GPS), and video surveillance technology can be integrated to establish a network for industrial pollution treatment. This network will allow for the real-time collection of environmental quality information, enabling the creation of a comprehensive, multi-level ecological monitoring network with full coverage.

To achieve this, sensor technology, wireless network technology, video recognition technology, and the Internet can be employed to develop a Cloud Platform for Environmental Awareness of the Internet of Things (IoT). This platform will enable the real-time dynamic perception of environmental quality.

Additionally, an environmental protection IoT data transmission system can be implemented by constructing both wired and wireless data paths to upload sensor and terminal monitoring data to the management platform. The overarching goal of this endeavor will be to achieve full intelligence in environmental protection businesses, including environmental quality monitoring, supervision, law enforcement against environmental pollution, and management decision-making. This will be made possible through the utilization of massive data resource service platforms.