Human action recognation model with accuracy 99% with KTH, WVU, IXMAS, WEIZMANN, UCF101 and ActivityNet
Budget: $30 – $250 USD
Objective:
The goal of this project is to develop a highly accurate human action recognition (HAR) model, achieving a target accuracy of 99% on benchmark datasets including KTH, WVU, IXMAS, WEIZMANN, UCF101, and ActivityNet. This model will identify and classify human actions from video frames across various domains, offering robust generalization across datasets with diverse action categories, background settings, and recording conditions.
Datasets:
KTH: Contains six types of human actions (e.g., walking, jogging) recorded in controlled conditions.
WVU: Features multi-angle action sequences with varying lighting and backgrounds.
IXMAS: Multi-view dataset capturing various actions from five camera angles.
WEIZMANN: Covers ten action types performed by different actors with consistent backgrounds.
UCF101: Large-scale dataset with 101 diverse action categories across a range of settings.
ActivityNet: Contains untrimmed videos with complex actions, making it suitable for temporal action detection.
Approach:
Data Preprocessing: Standardize the input video frames by resizing, normalizing, and, if necessary, performing data augmentation to increase dataset diversity and improve generalization.
Feature Extraction: Utilize deep learning architectures (e.g., Convolutional Neural Networks (CNNs) for spatial features, and Recurrent Neural Networks (RNNs) or 3D CNNs for temporal features) to capture meaningful representations of actions.
Model Architecture: Implement a hybrid deep learning model that combines 3D CNN and LSTM layers, allowing the model to learn spatial and temporal dynamics in video sequences effectively. Transfer learning with pre-trained networks (e.g., ResNet or Inception) will also be explored to improve performance on specific datasets.
Training and Fine-tuning: Train the model on individual datasets and fine-tune using cross-dataset training to ensure high accuracy across all datasets. Regularization techniques (e.g., dropout) and optimization methods (e.g., Adam) will be employed to prevent overfitting and accelerate convergence.
Evaluation: Use accuracy, precision, recall, and F1-score as metrics to evaluate model performance on each dataset. Cross-dataset validation will ensure that the model performs consistently across diverse datasets.
Expected Outcome:
This project aims to produce a robust HAR model capable of achieving 99% accuracy across the KTH, WVU, IXMAS, WEIZMANN, UCF101, and ActivityNet datasets. The model’s high accuracy will demonstrate its effectiveness in real-world applications such as video surveillance, sports analysis, and human-computer interaction.
Challenges:
Handling variations in camera angles, lighting, and background across datasets.
Developing a model that generalizes well to both controlled (e.g., KTH) and untrimmed, complex-action datasets (e.g., ActivityNet).
Applications: This HAR model can be applied to fields like intelligent video surveillance, automated sports analytics, and interactive gaming, where real-time and accurate action recognition is critical.
Focus will be on computer vision protection systems. Priority will be given to accuracy as the primary evaluation metric.
The goal of this project is to develop a highly accurate human action recognition (HAR) model, achieving a target accuracy of 99% on benchmark datasets including KTH, WVU, IXMAS, WEIZMANN, UCF101, and ActivityNet. This model will identify and classify human actions from video frames across various domains, offering robust generalization across datasets with diverse action categories, background settings, and recording conditions.
Datasets:
KTH: Contains six types of human actions (e.g., walking, jogging) recorded in controlled conditions.
WVU: Features multi-angle action sequences with varying lighting and backgrounds.
IXMAS: Multi-view dataset capturing various actions from five camera angles.
WEIZMANN: Covers ten action types performed by different actors with consistent backgrounds.
UCF101: Large-scale dataset with 101 diverse action categories across a range of settings.
ActivityNet: Contains untrimmed videos with complex actions, making it suitable for temporal action detection.
Approach:
Data Preprocessing: Standardize the input video frames by resizing, normalizing, and, if necessary, performing data augmentation to increase dataset diversity and improve generalization.
Feature Extraction: Utilize deep learning architectures (e.g., Convolutional Neural Networks (CNNs) for spatial features, and Recurrent Neural Networks (RNNs) or 3D CNNs for temporal features) to capture meaningful representations of actions.
Model Architecture: Implement a hybrid deep learning model that combines 3D CNN and LSTM layers, allowing the model to learn spatial and temporal dynamics in video sequences effectively. Transfer learning with pre-trained networks (e.g., ResNet or Inception) will also be explored to improve performance on specific datasets.
Training and Fine-tuning: Train the model on individual datasets and fine-tune using cross-dataset training to ensure high accuracy across all datasets. Regularization techniques (e.g., dropout) and optimization methods (e.g., Adam) will be employed to prevent overfitting and accelerate convergence.
Evaluation: Use accuracy, precision, recall, and F1-score as metrics to evaluate model performance on each dataset. Cross-dataset validation will ensure that the model performs consistently across diverse datasets.
Expected Outcome:
This project aims to produce a robust HAR model capable of achieving 99% accuracy across the KTH, WVU, IXMAS, WEIZMANN, UCF101, and ActivityNet datasets. The model’s high accuracy will demonstrate its effectiveness in real-world applications such as video surveillance, sports analysis, and human-computer interaction.
Challenges:
Handling variations in camera angles, lighting, and background across datasets.
Developing a model that generalizes well to both controlled (e.g., KTH) and untrimmed, complex-action datasets (e.g., ActivityNet).
Applications: This HAR model can be applied to fields like intelligent video surveillance, automated sports analytics, and interactive gaming, where real-time and accurate action recognition is critical.
Focus will be on computer vision protection systems. Priority will be given to accuracy as the primary evaluation metric.