  In this paper, we study the value of using synthetically produced videos as
training data for neural networks used for action categorization. Motivated by
the fact that texture and background of a video play little to no significant
roles in optical flow, we generated simplified texture-less and background-less
videos and utilized the synthetic data to train a Temporal Segment Network
(TSN). The results demonstrated that augmenting TSN with simplified synthetic
data improved the original network accuracy (68.5%), achieving 71.8% on HMDB-51
when adding 4,000 videos and 72.4% when adding 8,000 videos. Also, training
using simplified synthetic videos alone on 25 classes of UCF-101 achieved
30.71% when trained on 2500 videos and 52.7% when trained on 5000 videos.
Finally, results showed that when reducing the number of real videos of UCF-25
to 10% and combining them with synthetic videos, the accuracy drops to only
85.41%, compared to a drop to 77.4% when no synthetic data is added.
