  Existing approaches for spatio-temporal action detection in videos are
limited by the spatial extent and temporal duration of the actions. In this
paper, we present a modular system for spatio-temporal action detection in
untrimmed security videos. We propose a two stage approach. The first stage
generates dense spatio-temporal proposals using hierarchical clustering and
temporal jittering techniques on frame-wise object detections. The second stage
is a Temporal Refinement I3D (TRI-3D) network that performs action
classification and temporal refinement on the generated proposals. The object
detection-based proposal generation step helps in detecting actions occurring
in a small spatial region of a video frame, while temporal jittering and
refinement helps in detecting actions of variable lengths. Experimental results
on the spatio-temporal action detection dataset - DIVA - show the effectiveness
of our system. For comparison, the performance of our system is also evaluated
on the THUMOS14 temporal action detection dataset.
