  Activity detection in surveillance videos is a challenging task caused by
small objects, complex activity categories, its untrimmed nature, etc. Existing
methods are generally limited in performance due to inaccurate proposals, poor
classifiers or inadequate post-processing method. In this work, we propose a
comprehensive and effective activity detection system in untrimmed surveillance
videos for person-centered and vehicle-centered activities. It consists of four
modules, i.e., object localizer, proposal filter, activity classifier and
activity refiner. For person-centered activities, a novel part-attention
mechanism is proposed to explore detailed features in different body parts. As
for vehicle-centered activities, we propose a localization masking method to
jointly encode motion and foreground attention features. We conduct experiments
on the large-scale activity detection datasets VIRAT, and achieve the best
results for both groups of activities. Furthermore, our team won the 1st place
in the TRECVID 2021 ActEV challenge.
