ABSTRACT
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 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.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the author(s).
Notes
1 All results in the form of videos are available at https://www.youtube.com/playlist?list=PLJtUItpEnvbNQaGvVqF-bZYtn_75wR212
Additional information
Notes on contributors
![](/cms/asset/4638686b-33de-4541-8060-75edfe16e1c9/tijr_a_1729258_ilg0001.gif)
Jyoti Nigam
Jyoti Nigam is a PhD student in School of Computing and Electrical Engineering (SCEE) at Indian Institute of Technology, India. Her research interest is computer vision.
![](/cms/asset/9d65ff92-5caf-4637-90d6-6d710d8c170c/tijr_a_1729258_ilg0002.gif)
Renu M. Rameshan
Renu M Rameshan is an assistant professor in School of Computing and Electrical Engineering (SCEE) at Indian Institute of Technology, India. Her research interest is computer vision, image processing and ill-posed problems. Email: [email protected]