Fast Scale Invariant Tracker and Re-identification for First-Person Social Videos
Jyoti Nigam, Renu Rameshan · IETE Journal of Research · 2020
We address the problem of pedestrian tracking in videos of crowded scene which are captured by first-person viewpoint. The constant motion of camera and pedestrian makes this task challenging. The prime challenges are natural head motion of wearer and target loss and reappearance in a later frame, due to frequent changes in field of view. We propose that the use of first-person vision specific optical flow information and also the modification in the update process along with search region of trackers are useful to identify a lost target in a later frame. This process is termed re-identification in this paper. The specific trackers modified are MEEM and STRUCK. In addition to re-identification we achieve scale invariant tracking (upto 50% scale variation) and speed up by a factor of 2. We name our tracker as EgoTracker, since it utilizes the information which is specific to egocentric vision.