PhDTracker-level fusion for video tracking combines outputs (state estimations) from multiple
trackers, to address the shortcomings of individual trackers. Furthermore, performance evaluation
of trackers at run time (online) can determine low performing trackers that can be removed
from the fusion. This thesis presents a tracker-level fusion framework that performs online tracking
performance evaluation for fusion.
We first introduce a method to determine time instants of tracker failure that is divided into
two steps. First, we evaluate tracking performance by comparing the distributions of the tracker
state and a region around the state. We use Distribution Fields to generate the distributions of
both regions and compute a tracking performance score by comparing the distributions using the
L1 distance. Then, we model this score as a time series and employ the Auto Regressive Moving
Average method to forecast future values of the performance score. A difference between the
original and forecast returns the forecast error signal that we use to detect tracking failure. We
test the method with different datasets and then demonstrate its flexibility using tracking results
and sequences from the Visual Object Tracking (VOT) challenge.
The second part presents a tracker-level fusion method that combines the outputs of multiple
trackers. The method is divided into three steps. First, we group trackers into clusters based on
the spatio-temporal pair-wise relationships of their outputs. Then, we evaluate tracking performance
based on reverse-time analysis with an adaptive reference frame and define the cluster
with trackers that appear to be successfully following the target as the on-target cluster. Finally,
we fuse the outputs of the trackers in the on-target cluster to obtain the final target state. The
fusion approach uses standard tracker outputs and can therefore combine various types of trackers.
We test the method with several combinations of state-of-the-art trackers, and also compare
it with individual trackers and other fusion approaches.EACEA, under the EMJD ICE Project
