Project Background:
Human Motion Recognition revolves around leveraging advanced computer vision and machine learning technologies to interpret and understand human movements from visual input data. This interdisciplinary field aims to enable systems to recognize, analyze, and interpret the dynamic gestures and motions of individuals captured through cameras or other sensors. Human Motion Recognition has diverse applications ranging from healthcare and sports biomechanics to surveillance and human-computer interaction. This work involves developing algorithms and models that can discern intricate patterns and features in video or sensor data to identify specific actions, gestures, or activities. The overarching goal is to provide machines with the capability to comprehend and respond to human movements, leading to advancements in gesture-based interfaces, augmented reality, and personalized healthcare monitoring.
Problem Statement
- The problem statement in Human Motion Recognition stems from the challenges associated with accurately interpreting and understanding diverse human movements using computational methods.
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Varied contexts, environmental conditions, and individual differences contribute to the complexity of recognizing human motion.
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Challenges include addressing the intricacies of different movement patterns, handling occlusions in crowded environments, and ensuring robustness to variations in lighting conditions.
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Furthermore, real-time processing requirements and the need for accurate spatial-temporal modeling pose additional difficulties.
- It involves addressing scalability issues, adaptability to different environments, and ensuring the reliability of recognition across various types of movements.
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Additionally, considerations related to the efficient integration of motion recognition technology into practical applications add complexity to the problem statement.
Aim and Objectives
- Develop robust Human Motion Recognition systems using computer vision and machine learning techniques to interpret and respond to diverse human movements accurately.
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Create advanced algorithms for accurately recognizing and tracking human movements in various contexts.
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Achieve real-time processing capabilities to enable instantaneous recognition and response to human motions.
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Ensure scalability across different scenarios and environments, accommodating variations in movements and contexts.
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Develop models capable of capturing human motions spatial and temporal dynamics for improved accuracy.
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Design systems that can adapt to different lighting conditions, occlusions, and variations in individual movement patterns.
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Implement specialized modules for recognizing specific gestures or actions, enhancing the versatility of the system.
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Integrate Human Motion Recognition technology into practical applications such as human-computer interaction, gaming, or healthcare monitoring.
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Enhance the robustness of recognition systems to environmental factors, ensuring consistent performance in diverse settings.
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Develop interfaces that facilitate user interaction through recognized human motions, contributing to improved user experience.
Contributions to Human Motion Recognition
1. Developing robust algorithms capable of accurately recognizing and tracking human movements across diverse scenarios contributes to improved reliability.
2. Innovations in real-time processing capabilities, enabling instantaneous recognition and response to human motions, are particularly beneficial in interactive applications.
3. Development of systems that can adapt to variations in lighting conditions, handle occlusions, and perform consistently across different environments, improving overall robustness.
4. Contributions towards interfaces that leverage recognized human motions to facilitate user interaction, leading to improved user experience in various domains.
5. Exploration and integration of multimodal approaches, combining data from different sensors or modalities, enhance motion recognition systems accuracy and robustness.
6. Efforts to ensure inclusive designs that consider diverse human populations, accommodating variations in movements and ensuring that recognition systems are accessible to a wide range of users.
Deep Learning Algorithms for Human Motion Recognition
- Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks (RNNs)
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Long Short-Term Memory Networks (LSTMs)
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Autoencoders
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Capsule Networks
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Siamese Networks
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Graph Neural Networks (GNNs)
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Variational Autoencoders (VAEs)
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Spatial-Temporal Graph Networks (ST-GCN)
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Quaternary Residual Networks (QuaterNet)
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Attention Mechanisms for Human Pose Estimation
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Temporal Convolutional Networks (TCNs)
Datasets for Human Motion Recognition
- NTU RGB+D Dataset
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MPII Human Pose Dataset
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UCF101 - Action Recognition Data
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Kinetics - Human Action Video Dataset
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PKU-MMD Dataset
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SYSU 3D Human-Object Interaction Dataset
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Florence 3D Action Dataset
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MHAD (Multimodal Human Action Dataset)
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SBU Kinect Interaction Dataset
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CAD-60 and CAD-120 Datasets
Performance Metrics
- Accuracy
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Precision
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Recall
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F1 Score
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Area Under the Receiver Operating Characteristic curve (AUC-ROC)
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Area Under the Precision-Recall curve (AUC-PR)
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Mean Average Precision (mAP)
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Normalized Mutual Information (NMI)
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Cohen Kappa
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Root Mean Squared Error (RMSE) for 3D Pose Estimation
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
