Action recognition: From static datasets to moving robots

Fahimeh Rezazadegan, Sareh Rowlands, ben upcroft, Michael Milford

Abstract:

Deep learning models have achieved state-of-theart performance in recognizing human activities, but often rely
on utilizing background cues present in typical computer vision
datasets that predominantly have a stationary camera. If these
models are to be employed by autonomous robots in real world
environments, they must be adapted to perform independently
of background cues and camera motion effects. To address
these challenges, we propose a new method that firstly
generates generic action region proposals with good potential to
locate one human action in unconstrained videos regardless of
camera motion and then uses action proposals to extract and
classify effective shape and motion features by a ConvNet
framework. In a range of experiments, we demonstrate that by
actively proposing action regions during both training and
testing, state-of-the-art or better performance is achieved on
benchmarks. We show the outperformance of our approach
compared to the state-of-the-art in two new datasets; one
emphasizes on irrelevant background, the other highlights the
camera motion. We also validate our action recognition method
in an abnormal behavior detection scenario to improve
workplace safety. The results verify a higher success rate for
our method due to the ability of our system to recognize human
actions regardless of environment and camera motion.