  Activity detection in security videos is a difficult problem due to multiple
factors such as large field of view, presence of multiple activities, varying
scales and viewpoints, and its untrimmed nature. The existing research in
activity detection is mainly focused on datasets, such as UCF-101, JHMDB,
THUMOS, and AVA, which partially address these issues. The requirement of
processing the security videos in real-time makes this even more challenging.
In this work we propose Gabriella, a real-time online system to perform
activity detection on untrimmed security videos. The proposed method consists
of three stages: tubelet extraction, activity classification, and online
tubelet merging. For tubelet extraction, we propose a localization network
which takes a video clip as input and spatio-temporally detects potential
foreground regions at multiple scales to generate action tubelets. We propose a
novel Patch-Dice loss to handle large variations in actor size. Our online
processing of videos at a clip level drastically reduces the computation time
in detecting activities. The detected tubelets are assigned activity class
scores by the classification network and merged together using our proposed
Tubelet-Merge Action-Split (TMAS) algorithm to form the final action
detections. The TMAS algorithm efficiently connects the tubelets in an online
fashion to generate action detections which are robust against varying length
activities. We perform our experiments on the VIRAT and MEVA (Multiview
Extended Video with Activities) datasets and demonstrate the effectiveness of
the proposed approach in terms of speed (~100 fps) and performance with
state-of-the-art results. The code and models will be made publicly available.
