  Motion analysis plays a critical role in various applications, from virtual
reality and augmented reality to assistive visual navigation. Traditional
self-driving technologies, while advanced, typically do not translate directly
to pedestrian applications due to their reliance on extensive sensor arrays and
non-feasible computational frameworks. This highlights a significant gap in
applying these solutions to human users since human navigation introduces
unique challenges, including the unpredictable nature of human movement,
limited processing capabilities of portable devices, and the need for
directional responsiveness due to the limited perception range of humans. In
this project, we introduce an image-only method that applies motion analysis
using optical flow with ego-motion compensation to predict Motor Focus-where
and how humans or machines focus their movement intentions. Meanwhile, this
paper addresses the camera shaking issue in handheld and body-mounted devices
which can severely degrade performance and accuracy, by applying a Gaussian
aggregation to stabilize the predicted motor focus area and enhance the
prediction accuracy of movement direction. This also provides a robust,
real-time solution that adapts to the user's immediate environment.
Furthermore, in the experiments part, we show the qualitative analysis of motor
focus estimation between the conventional dense optical flow-based method and
the proposed method. In quantitative tests, we show the performance of the
proposed method on a collected small dataset that is specialized for motor
focus estimation tasks.
