Understanding and Diagnosing Visual Tracking Systems

PhD Thesis Proposal Defence


Title: "Understanding and Diagnosing Visual Tracking Systems"

by

Mr. Naiyan WANG


Abstract:

Several benchmark datasets for visual tracking research have been proposed in 
recent years.  Despite their usefulness, whether they are sufficient for 
understanding and diagnosing the strengths and weaknesses of different trackers 
remains questionable.  To address this issue, we propose a framework by 
breaking a tracker down into five constituent parts, namely, motion model, 
feature extractor, observation model, model updater, and ensemble 
post-processor.  We then conduct ablative experiments on each component to 
study how it affects the overall result.  Surprisingly, our findings are 
discrepant with some common beliefs in the visual tracking research community. 
We find that the feature extractor plays the most important role in a tracker. 
On the other hand, although the observation model is the focus of many studies, 
we find that it often brings no significant improvement.  Moreover, the motion 
model and model updater contain many details that could affect the result. 
Also, the ensemble post-processor can improve the result substantially when the 
constituent trackers have high diversity.  Based on our findings, we put 
together some very elementary building blocks to give a basic tracker which is 
competitive in performance to the state-of-the-art trackers.  We believe our 
framework can provide a solid baseline when conducting controlled experiments 
for visual tracking research.


Date:			Friday, 29 May 2015

Time:                   2:00pm - 4:00pm

Venue:                  Room 3584
                         lifts 27/28

Committee Members:	Prof. Dit-Yan Yeung (Supervisor)
  			Prof. Albert Chung (Chairperson)
 			Prof. Chiew-Lan Tai
  			Prof. Nevin Zhang


**** ALL are Welcome ****