A Framework Combining 3D CNN and Transformer for Video-Based Behavior Recognition
This work addresses video-based behavior recognition for applications like public safety and surveillance, but it is incremental as it combines existing methods.
The paper tackled the problem of video-based behavior recognition by proposing a hybrid framework combining 3D CNN and Transformer architectures to capture both local spatiotemporal features and long-range dependencies, resulting in higher recognition accuracy on benchmark datasets compared to traditional methods.
Video-based behavior recognition is essential in fields such as public safety, intelligent surveillance, and human-computer interaction. Traditional 3D Convolutional Neural Network (3D CNN) effectively capture local spatiotemporal features but struggle with modeling long-range dependencies. Conversely, Transformers excel at learning global contextual information but face challenges with high computational costs. To address these limitations, we propose a hybrid framework combining 3D CNN and Transformer architectures. The 3D CNN module extracts low-level spatiotemporal features, while the Transformer module captures long-range temporal dependencies, with a fusion mechanism integrating both representations. Evaluated on benchmark datasets, the proposed model outperforms traditional 3D CNN and standalone Transformers, achieving higher recognition accuracy with manageable complexity. Ablation studies further validate the complementary strengths of the two modules. This hybrid framework offers an effective and scalable solution for video-based behavior recognition.