Project Background:
Action recognition using deep learning centers on the endeavor to develop advanced systems capable of automatically detecting and understanding human actions from video data. With the advent of deep learning techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), this field has made significant progress. These methods have enabled researchers to extract spatial-temporal features from videos, allowing for more accurate and robust action recognition than traditional computer vision approaches. The applications of action recognition are vast and diverse, ranging from video surveillance and security to human-computer interactions and healthcare monitoring. Moreover, researchers are exploring techniques for fine-grained action understanding, which involves recognizing the type of action and its context and semantics. By leveraging deep learning algorithms, action recognition systems can achieve higher accuracy, efficiency, and scalability, paving the way for advancements in various fields such as robotics, autonomous vehicles, and augmented reality.
Problem Statement
- Develop methods for effectively capturing spatial-temporal features from video data to represent human actions accurately.
- Enhance the capability of action recognition systems to distinguish between subtle and complex actions and understand the context and semantics of actions within the scene.
- Optimize action recognition algorithms for real-time processing, enabling efficient analysis of video streams in applications such as surveillance, sports analytics, and human-computer interaction.
- Develop efficient methods for annotating large-scale video datasets with ground truth action labels, essential for training and evaluating deep learning models for action recognition.
- Investigate techniques for adapting action recognition models to new domains with limited labeled data, ensuring their applicability across diverse real-world scenarios.
- Explore methods for integrating multiple modalities to improve the robustness and accuracy of action recognition systems.
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Develop mechanisms for incorporating temporal reasoning and context modeling into action recognition algorithms, enabling them to capture temporal and long-range dependencies between actions.
Aim and Objectives
- Develop advanced deep learning-based models for accurate and efficient action recognition from video data.
- Enhance spatial-temporal feature learning for robust representation of human actions.
- Improve robustness to variability in lighting conditions, viewpoints, and occlusions.
- Enable fine-grained action understanding to distinguish subtle and complex actions.
- Optimize algorithms for real-time performance in video analysis applications.
- Develop methods for large-scale dataset annotation and domain adaptation.
- Explore multimodal fusion techniques to improve recognition accuracy.
- Incorporate temporal reasoning and context modeling for better action understanding.
Contributions to Action Recognition using Deep Learning
- Enhanced spatial-temporal feature learning for robust action representation.
- Efficient methods for large-scale dataset annotation and domain adaptation.
- Multimodal fusion techniques for improved recognition accuracy.
- Incorporation of temporal reasoning and context modeling.
- Addressing ethical considerations related to privacy, bias, and fairness.
- Enhanced interpretability and explainability of action recognition results.
Deep Learning Algorithms for Action Recognition
- 3D Convolutional Neural Networks (3D CNNs)
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Two-Stream Networks
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Temporal Convolutional Networks (TCNs)
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Long Short-Term Memory (LSTM) Networks
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Convolutional LSTM (ConvLSTM)
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Residual Networks (ResNets)
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Inflated 3D Convolutional Networks (I3D)
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SlowFast Networks
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Non-local Neural Networks
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Transformer-based models for video processing
Datasets for Action Recognition
- UCF101
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HMDB51
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Kinetics
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ActivityNet
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THUMOS
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Charades
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NTU RGB+D
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MPII Cooking Activities
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AVA (Atomic Visual Actions)
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
