  Video activity Recognition has recently gained a lot of momentum with the
release of massive Kinetics (400 and 600) data. Architectures such as I3D and
C3D networks have shown state-of-the-art performances for activity recognition.
The one major pitfall with these state-of-the-art networks is that they require
a lot of compute. In this paper we explore how we can achieve comparable
results to these state-of-the-art networks for devices-on-edge. We primarily
explore two architectures - I3D and Temporal Segment Network. We show that
comparable results can be achieved using one tenth the memory usage by changing
the testing procedure. We also report our results on Resnet architecture as our
backbone apart from the original Inception architecture. Specifically, we
achieve 84.54\% top-1 accuracy on UCF-101 dataset using only RGB frames.
