  The objective of the panoramic activity recognition task is to identify
behaviors at various granularities within crowded and complex environments,
encompassing individual actions, social group activities, and global
activities. Existing methods generally use either parameter-independent modules
to capture task-specific features or parameter-sharing modules to obtain common
features across all tasks. However, there is often a strong interrelatedness
and complementary effect between tasks of different granularities that previous
methods have yet to notice. In this paper, we propose a model called MPT-PAR
that considers both the unique characteristics of each task and the synergies
between different tasks simultaneously, thereby maximizing the utilization of
features across multi-granularity activity recognition. Furthermore, we
emphasize the significance of temporal and spatial information by introducing a
spatio-temporal relation-enhanced module and a scene representation learning
module, which integrate the the spatio-temporal context of action and global
scene into the feature map of each granularity. Our method achieved an overall
F1 score of 47.5\% on the JRDB-PAR dataset, significantly outperforming all the
state-of-the-art methods.
