Multilayer Neural Network for View Invariant Human Action Recognition

Problem Definition

Problem Description: One of the key challenges in human action recognition is the variability in viewpoint or perspective from which a human action is being observed. Current recognition systems often struggle to accurately identify and classify human actions when the viewpoint changes. This inconsistency in perspective hinders the performance and reliability of action recognition systems, especially in real-world scenarios where actions may be performed from different angles or orientations. To address this problem, the project "View Invariant Human Action Recognition Using Multilayer Neural Network" proposes a novel approach that aims to achieve view invariance in human action recognition. By extracting 3D skeletal joint locations from Kinect depth maps and utilizing a Multilayer neural network as a compact representation of postures, the project tackles the challenge of viewpoint variability.

The use of LDA for feature refinement and clustering into posture visual words further enhances the robustness of the proposed method. Therefore, the problem that this project aims to address is the lack of view invariance in current human action recognition systems. By developing a method that can accurately recognize human actions regardless of the viewpoint or perspective from which they are observed, the project aims to improve the performance and reliability of action recognition systems in various real-world applications.

Proposed Work

The proposed work titled "View Invariant Human Action Recognition Using Multilayer Neural Network" focuses on developing a method for human action recognition utilizing Multilayer neural network as a representation of postures. The research involves extracting 3D skeletal joint locations from Kinect depth maps and computing Multilayer neural networks from the action depth sequences. These networks are further processed using LDA and clustered into k posture visual words, representing prototypical poses of actions. One of the key features of this approach is its ability to demonstrate significant view invariance due to the design of a spherical coordinate system and robust 3D skeleton estimation from Kinect. The project utilizes Basic Matlab and Artificial Neural Network modules and falls under the categories of Image Processing & Computer Vision, Latest Projects, MATLAB Based Projects, and Optimization & Soft Computing Techniques.

It also aligns with subcategories such as Neural Network, MATLAB Projects Software, Image Classification, Image Recognition, and Real Time Application Control Systems.

Application Area for Industry

The project "View Invariant Human Action Recognition Using Multilayer Neural Network" can be applied in various industrial sectors such as surveillance, security, healthcare, sports analysis, and robotics. In surveillance and security, the project's proposed solutions can be used to accurately identify and classify human actions from different viewpoints, enhancing the effectiveness of monitoring systems. In healthcare, the system can be utilized for patient monitoring and rehabilitation exercises, ensuring accurate tracking of movements regardless of the perspective. In sports analysis, the project can aid in evaluating player performance and training by recognizing actions accurately from various angles. Additionally, in robotics, the system can help in developing robots that can understand and mimic human actions effectively.

The challenges faced by industries in accurately recognizing human actions from changing viewpoints can be addressed by implementing the solutions proposed in this project. By achieving view invariance through the use of Multilayer neural networks and 3D skeletal joint locations from Kinect depth maps, the project offers a robust method for action recognition. The benefits of implementing these solutions include improved performance and reliability of action recognition systems in real-world applications. The proposed approach can enhance the efficiency of surveillance systems, optimize patient monitoring in healthcare settings, provide valuable insights in sports analysis, and enable more advanced capabilities in robotics. Overall, the project's solutions have the potential to revolutionize human action recognition across various industrial domains.

Application Area for Academics

The proposed project on "View Invariant Human Action Recognition Using Multilayer Neural Network" holds significant potential for research by MTech and PHD students in the fields of Image Processing & Computer Vision, Latest Projects, MATLAB Based Projects, and Optimization & Soft Computing Techniques. The relevance of this project lies in addressing the challenge of viewpoint variability in human action recognition systems, a key issue that hinders the performance and reliability of current systems. MTech and PHD students can utilize the code and literature of this project to explore innovative research methods, simulations, and data analysis for their dissertations, theses, or research papers. By utilizing Multilayer neural networks and LDA for feature refinement, researchers can explore novel approaches to achieving view invariance in human action recognition. The project's focus on extracting 3D skeletal joint locations from Kinect depth maps and clustering them into posture visual words provides a robust framework for recognizing human actions regardless of the viewpoint.

MTech students and PHD scholars can further enhance this method by incorporating additional techniques or modifications to improve its performance and applicability in real-world scenarios. The project's technology integration with Basic Matlab and Artificial Neural Network modules offers a practical and accessible platform for researchers to work on. By delving into categories like Image Classification, Image Recognition, and Real Time Application Control Systems, students can apply this project to various research domains and explore new possibilities for enhancing human action recognition systems. There is also a scope for future research in optimizing the Multilayer neural network architecture, refining the clustering algorithms for posture visual words, and expanding the dataset for comprehensive testing and validation. Overall, the proposed project provides a valuable opportunity for MTech and PHD students to engage in cutting-edge research and contribute to the advancement of human action recognition technology.

Keywords

view invariant human action recognition, multilayer neural network, 3D skeletal joint locations, Kinect depth maps, viewpoint variability, LDA feature refinement, posture visual words, action recognition systems, real-world scenarios, human action classification, perspective variability, robustness enhancement, neural network representation, prototypical poses, spherical coordinate system, image processing, computer vision, MATLAB projects, optimization techniques, soft computing, image classification, real-time application control systems