  Deep learning models have achieved state-of-the- art 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.
