  Activity detection from first-person videos (FPV) captured using a wearable
camera is an active research field with potential applications in many sectors,
including healthcare, law enforcement, and rehabilitation. State-of-the-art
methods use optical flow-based hybrid techniques that rely on features derived
from the motion of objects from consecutive frames. In this work, we developed
a two-stream network, the \emph{SegCodeNet}, that uses a network branch
containing video-streams with color-coded semantic segmentation masks of
relevant objects in addition to the original RGB video-stream. We also include
a stream-wise attention gating that prioritizes between the two streams and a
frame-wise attention module that prioritizes the video frames that contain
relevant features. Experiments are conducted on an FPV dataset containing $18$
activity classes in office environments. In comparison to a single-stream
network, the proposed two-stream method achieves an absolute improvement of
$14.366\%$ and $10.324\%$ for averaged F1 score and accuracy, respectively,
when average results are compared for three different frame sizes
$224\times224$, $112\times112$, and $64\times64$. The proposed method provides
significant performance gains for lower-resolution images with absolute
improvements of $17\%$ and $26\%$ in F1 score for input dimensions of
$112\times112$ and $64\times64$, respectively. The best performance is achieved
for a frame size of $224\times224$ yielding an F1 score and accuracy of
$90.176\%$ and $90.799\%$ which outperforms the state-of-the-art Inflated 3D
ConvNet (I3D) \cite{carreira2017quo} method by an absolute margin of $4.529\%$
and $2.419\%$, respectively.
