Project Background:
The behavior recognition using deep learning involves the application of advanced machine learning techniques to analyze and understand human actions and behaviors from various data sources, consists of images, videos, text, or sensor data. Deep learning with its ability to automatically extract complex features and patterns has revolutionized the field of behavior recognition. This technology finds significant relevance in diverse domains including surveillance, healthcare, autonomous systems, among others. This aims to address the challenges of recognizing and interpreting human actions and emotions accurately which is crucial for tasks like security monitoring, gesture-based interfaces, and mental health assessment.
Problem Statement
- The behavior recognition using deep learning centers around the need to develop accurate and robust systems for automated behavior analysis.
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Traditional methods for behavior recognition struggle to handle the complexity and variability of human or object behaviors, often relying on handcrafted features that may not adapt well to diverse scenarios.
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Challenges include the collection of large labeled datasets, designing deep neural architectures suitable for the specific behavior recognition task, addressing issues of scalability and real-time processing.
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Also, the problem revolves around harnessing the potential of deep learning to build accurate, efficient, and ethical behavior recognition systems that can be deployed in practical, real-world settings.
Aim and Objectives
- Develop accurate and scalable deep learning models for behavior analysis.
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Create diverse and labeled datasets for training and evaluation.
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Ensure real-time processing capabilities for practical applications.
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Address interpretability and ethical concerns.
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Enhance behavior recognition across various domains including security, healthcare, and human-computer interaction.
Contributions to Behavior Recognition
1. This project work significantly improves the accuracy of behavior recognition across various domains.
2. Scalable solutions that can handle diverse and complex behaviors in large and diversified datasets.
3. An advancements in model interpretability and transparency for better trust and ethical considerations.
4. Extending behavior recognition to security, healthcare, and human-computer interaction, enhancing multiple aspects of daily life and industry operations.
Deep Learning Algorithms for Behavior Recognition
- Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks (RNNs)
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Long Short-Term Memory (LSTM)
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Gated Recurrent Unit
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3D Convolutional Networks
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Temporal Convolutional Networks
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Transformer Models
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Siamese Networks
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Generative Adversarial Networks
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Capsule Networks
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Graph Neural Networks
Datasets for Behavior Recognition
- UCF101
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HMDB51
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Kinetics
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NTU RGB+D
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MPII Human Pose
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Berkeley MHAD (Multimodal Human Action Database)
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AFEW (Acted Facial Expressions in the Wild)
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DALY (Daily Action Localization in YouTube)
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UT-Interaction
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EmoReact
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TRECVID Multimedia Event Detection (MED) Test Collection
Performance Metrics
- Accuracy
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Precision
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Recall
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F1-Score
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Mean Average Precision (mAP)
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Intersection over Union (IoU)
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Cohens Kappa
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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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Root Mean Square Error (RMSE)
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Mean Absolute Error (MAE)
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Intra-class Correlation Coefficient (ICC)
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
