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A 3DCNN-Based Knowledge Distillation Framework for Human Activity Recognition
1Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA.
Journal of Imaging
|April 27, 2023
Summary
This study introduces a knowledge distillation framework for human action recognition, significantly improving accuracy and inference speed. The method efficiently transfers knowledge from a large teacher model to a smaller student model for real-time applications.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Human action recognition is crucial for video analytics, requiring analysis of complex sequential patterns in human movements.
- Existing methods often struggle with balancing accuracy and computational efficiency for real-time applications.
Purpose of the Study:
- To propose an effective knowledge distillation framework for enhancing human action recognition.
- To distill spatio-temporal knowledge from a large teacher model to a lightweight student model.
Main Methods:
- Developed an offline knowledge distillation framework using a pre-trained 3D Convolutional Neural Network (3DCNN) teacher and a lightweight 3DCNN student model.
- Trained only the student model to mimic the teacher's prediction accuracy.
Main Results:
- Achieved up to 35% improvement in accuracy compared to state-of-the-art methods on four benchmark datasets.
- Demonstrated up to 50x improvement in inference speed (Frames Per Second - FPS) over existing methods.
Conclusions:
- The proposed knowledge distillation framework offers a robust and efficient solution for human action recognition.
- The method's high accuracy and fast inference times make it suitable for real-time human activity recognition applications.
