SegCodeNet: Color-Coded Segmentation Masks for Activity Detection from Wearable Cameras

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.