Group Activity Recognition using Unreliable Tracked Pose
Haritha Thilakarathne Aiden Nibali Zhen He Stuart Morgan
Group activity recognition in video is a complex task due to the need for a
model to recognise the actions of all individuals in the video and their
complex interactions. Recent studies propose that optimal performance is
achieved by individually tracking each person and subsequently inputting the
sequence of poses or cropped images/optical flow into a model. This helps the
model to recognise what actions each person is performing before they are
merged to arrive at the group action class. However, all previous models are
highly reliant on high quality tracking and have only been evaluated using
ground truth tracking information. In practice it is almost impossible to
achieve highly reliable tracking information for all individuals in a group
activity video. We introduce an innovative deep learning-based group activity
recognition approach called Rendered Pose based Group Activity Recognition
System (RePGARS) which is designed to be tolerant of unreliable tracking and
pose information. Experimental results confirm that RePGARS outperforms all
existing group activity recognition algorithms tested which do not use ground
truth detection and tracking information.