EE ZOOM Seminar: Long-term Unsupervised Tracking with GOTURN
השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז. בצ'אט
Join Zoom Meeting
https://zoom.us/j/99387669018?pwd=bEttWTlSZEJ3UjgycnV5RkpzbkxKUT09
Meeting ID: 993 8766 9018
Password: 291827
Speaker: Guy Adler
M.Sc. student under the supervision of Prof. Shai Avidan
Monday, May 18th, 2020 at 15:00
ZOOM
Long-term Unsupervised Tracking with GOTURN
Abstract
Visual object tracking faces many challenges when adapting to varying conditions. Objects can deform, perform out of plane rotations, become partially occluded or even leave the scene entirely only to return many frames later. The quality of long-term tracking depends on the robustness of the tracker to such disruptions. To overcome these challenges, machine learning algorithms require a large amount of annotated data. This work seeks to decrease the amount of annotation required for the tracking task by implementing unsupervised training methods.
None of the existing methods are candidates for utilizing end-to-end learning with unsupervised learning for improving its decision making, and thus are not able to generalize well enough. We try to address this shortage of learnable long-term methods by enhancing the efficient and simple GOTURN tracker by adding a spatial transformer, a module that allows estimation of affine transformations between images. Combining these two methods and adding memory to the system (in the form of RNNs) will allow propagation of information from a few labelled frames to the entire sequence, thus enabling end-to-end semi-supervised training of a general object tracker.
