Spatio-Temporal Proximity-Aware Dual-Path Model for Panoramic Activity Recognition
This work addresses panoramic activity recognition, which is important for applications like surveillance and robotics, but it appears incremental as it builds on existing hierarchical approaches by introducing a dual-path architecture.
The paper tackled the problem of recognizing diverse human activities in crowded panoramic scenes by proposing a dual-path network that incorporates spatio-temporal proximity, achieving a state-of-the-art overall F1 score of 46.5% on the JRDB-PAR dataset.
Panoramic Activity Recognition (PAR) seeks to identify diverse human activities across different scales, from individual actions to social group and global activities in crowded panoramic scenes. PAR presents two major challenges: 1) recognizing the nuanced interactions among numerous individuals and 2) understanding multi-granular human activities. To address these, we propose Social Proximity-aware Dual-Path Network (SPDP-Net) based on two key design principles. First, while previous works often focus on spatial distance among individuals within an image, we argue to consider the spatio-temporal proximity. It is crucial for individual relation encoding to correctly understand social dynamics. Secondly, deviating from existing hierarchical approaches (individual-to-social-to-global activity), we introduce a dual-path architecture for multi-granular activity recognition. This architecture comprises individual-to-global and individual-to-social paths, mutually reinforcing each other's task with global-local context through multiple layers. Through extensive experiments, we validate the effectiveness of the spatio-temporal proximity among individuals and the dual-path architecture in PAR. Furthermore, SPDP-Net achieves new state-of-the-art performance with 46.5\% of overall F1 score on JRDB-PAR dataset.