Abstract:
In this thesis we present a novel approach of developing correspondence cost matrix
to improve the data association phase by fusing data from multiple sources. Our cost
matrix is composed of both position and visual cues and provides better performance
than existing online state of the art trackers. The proposed algorithm provides more than
realtime performance and can be deployed in computationally restrictive environments.
Furthermore vectorized implementations of the proposed algorithm can allow for large
scale real-time tracking which makes it for industrial usage.
To the best of my knowledge no real time online tracker provides better performance
trade off than the proposed algorithm. The employed technique seamlessly
blends deep learning, a more advanced approach with Hungarian Algorithm, a rather
primitive cost assignment algorithm, in order to improve the performance of tracking in
a tracking by detection framework. The algorithm has been benchmarked on the MOT
17 Challenge, which is a standard benchmarking tool for all tracking algorithms and
has alot of challenging sequences.
