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 $224times224$, $112times112$, and $64times64$. 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 $112times112$ and $64times64$, respectively. The best performance is achieved for a frame size of $224times224$ 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.
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