  Panoramic Activity Recognition (PAR) aims to identify multi-granularity
behaviors performed by multiple persons in panoramic scenes, including
individual activities, group activities, and global activities. Previous
methods 1) heavily rely on manually annotated detection boxes in training and
inference, hindering further practical deployment; or 2) directly employ normal
detectors to detect multiple persons with varying size and spatial occlusion in
panoramic scenes, blocking the performance gain of PAR. To this end, we
consider learning a detector adapting varying-size occluded persons, which is
optimized along with the recognition module in the all-in-one framework.
Therefore, we propose a novel Adapt-Focused bi-Propagating Prototype learning
(AdaFPP) framework to jointly recognize individual, group, and global
activities in panoramic activity scenes by learning an adapt-focused detector
and multi-granularity prototypes as the pretext tasks in an end-to-end way.
Specifically, to accommodate the varying sizes and spatial occlusion of
multiple persons in crowed panoramic scenes, we introduce a panoramic
adapt-focuser, achieving the size-adapting detection of individuals by
comprehensively selecting and performing fine-grained detections on
object-dense sub-regions identified through original detections. In addition,
to mitigate information loss due to inaccurate individual localizations, we
introduce a bi-propagation prototyper that promotes closed-loop interaction and
informative consistency across different granularities by facilitating
bidirectional information propagation among the individual, group, and global
levels. Extensive experiments demonstrate the significant performance of AdaFPP
and emphasize its powerful applicability for PAR.
