When Spatial meets Temporal in Action Recognition
This work addresses the challenge of comprehensive spatiotemporal integration in video action recognition, which is crucial for improving accuracy in tasks like surveillance or human-computer interaction, but it appears incremental as it builds on existing models with a novel preprocessing method.
The paper tackles the problem of effectively integrating spatial and temporal information in video action recognition by introducing the Temporal Integration and Motion Enhancement (TIME) layer, a preprocessing technique that rearranges video frames into a spatial grid to balance both aspects, and demonstrates its effectiveness by enhancing recognition accuracy when integrated into popular models like ResNet-50 and Vision Transformer.
Video action recognition has made significant strides, but challenges remain in effectively using both spatial and temporal information. While existing methods often focus on either spatial features (e.g., object appearance) or temporal dynamics (e.g., motion), they rarely address the need for a comprehensive integration of both. Capturing the rich temporal evolution of video frames, while preserving their spatial details, is crucial for improving accuracy. In this paper, we introduce the Temporal Integration and Motion Enhancement (TIME) layer, a novel preprocessing technique designed to incorporate temporal information. The TIME layer generates new video frames by rearranging the original sequence, preserving temporal order while embedding $N^2$ temporally evolving frames into a single spatial grid of size $N \times N$. This transformation creates new frames that balance both spatial and temporal information, making them compatible with existing video models. When $N=1$, the layer captures rich spatial details, similar to existing methods. As $N$ increases ($N\geq2$), temporal information becomes more prominent, while the spatial information decreases to ensure compatibility with model inputs. We demonstrate the effectiveness of the TIME layer by integrating it into popular action recognition models, such as ResNet-50, Vision Transformer, and Video Masked Autoencoders, for both RGB and depth video data. Our experiments show that the TIME layer enhances recognition accuracy, offering valuable insights for video processing tasks.