  Group Activity Recognition (GAR) aims to detect the activity performed by
multiple actors in a scene. Prior works model the spatio-temporal features
based on the RGB, optical flow or keypoint data types. However, using both the
temporality and these data types altogether increase the computational
complexity significantly. Our hypothesis is that by only using the RGB data
without temporality, the performance can be maintained with a negligible loss
in accuracy. To that end, we propose a novel GAR technique for volleyball
videos, DECOMPL, which consists of two complementary branches. In the visual
branch, it extracts the features using attention pooling in a selective way. In
the coordinate branch, it considers the current configuration of the actors and
extracts the spatial information from the box coordinates. Moreover, we
analyzed the Volleyball dataset that the recent literature is mostly based on,
and realized that its labeling scheme degrades the group concept in the
activities to the level of individual actors. We manually reannotated the
dataset in a systematic manner for emphasizing the group concept. Experimental
results on the Volleyball as well as Collective Activity (from another domain,
i.e., not volleyball) datasets demonstrated the effectiveness of the proposed
model DECOMPL, which delivered the best/second best GAR performance with the
reannotations/original annotations among the comparable state-of-the-art
techniques. Our code, results and new annotations will be made available
through GitHub after the revision process.
