We propose a tracker-independent framework to determine time instants when a
video tracker fails. The framework is divided into two steps. First, we
determine tracking quality by comparing the distributions of the tracker state
and a region around the state. We generate the distributions using Distribution
Fields and compute a tracking quality 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
quality score. A difference between the original and forecast returns an error
signal that we use to detect a tracker failure. We validate the proposed
approach over different datasets and demonstrate its flexibility with tracking
results and sequences from the Visual Object Tracking (VOT) challenge.
Obaidullah Khalid received his PhD from Qeen Mary University of London.
Currently he his visiting Honeywell, Prague under EU FP7 project CENTAUR.