  Recent advancements in cabled ocean observatories have increased the quality
and prevalence of underwater videos; this data enables the extraction of
high-level biologically relevant information such as species' behaviours.
Despite this increase in capability, most modern methods for the automatic
interpretation of underwater videos focus only on the detection and counting
organisms. We propose an efficient computer vision- and deep learning-based
method for the detection of biological behaviours in videos. TempNet uses an
encoder bridge and residual blocks to maintain model performance with a
two-staged, spatial, then temporal, encoder. TempNet also presents temporal
attention during spatial encoding as well as Wavelet Down-Sampling
pre-processing to improve model accuracy. Although our system is designed for
applications to diverse fish behaviours (i.e, is generic), we demonstrate its
application to the detection of sablefish (Anoplopoma fimbria) startle events.
We compare the proposed approach with a state-of-the-art end-to-end video
detection method (ReMotENet) and a hybrid method previously offered exclusively
for the detection of sablefish's startle events in videos from an existing
dataset. Results show that our novel method comfortably outperforms the
comparison baselines in multiple metrics, reaching a per-clip accuracy and
precision of 80% and 0.81, respectively. This represents a relative improvement
of 31% in accuracy and 27% in precision over the compared methods using this
dataset. Our computational pipeline is also highly efficient, as it can process
each 4-second video clip in only 38ms. Furthermore, since it does not employ
features specific to sablefish startle events, our system can be easily
extended to other behaviours in future works.
