  Current data analysis for the Canadian Olympic fencing team is primarily done
manually by coaches and analysts. Due to the highly repetitive, yet dynamic and
subtle movements in fencing, manual data analysis can be inefficient and
inaccurate. We propose FenceNet as a novel architecture to automate the
classification of fine-grained footwork techniques in fencing. FenceNet takes
2D pose data as input and classifies actions using a skeleton-based action
recognition approach that incorporates temporal convolutional networks to
capture temporal information. We train and evaluate FenceNet on the Fencing
Footwork Dataset (FFD), which contains 10 fencers performing 6 different
footwork actions for 10-11 repetitions each (652 total videos). FenceNet
achieves 85.4% accuracy under 10-fold cross-validation, where each fencer is
left out as the test set. This accuracy is within 1% of the current
state-of-the-art method, JLJA (86.3%), which selects and fuses features
engineered from skeleton data, depth videos, and inertial measurement units.
BiFenceNet, a variant of FenceNet that captures the "bidirectionality" of human
movement through two separate networks, achieves 87.6% accuracy, outperforming
JLJA. Since neither FenceNet nor BiFenceNet requires data from wearable
sensors, unlike JLJA, they could be directly applied to most fencing videos,
using 2D pose data as input extracted from off-the-shelf 2D human pose
estimators. In comparison to JLJA, our methods are also simpler as they do not
require manual feature engineering, selection, or fusion.

Computer Vision and Pattern Recognition