Visual Tracking with Autoencoder-Based Maximum A Posteriori Data Fusion

Authors

Yevgeniy Reznichenko, Marquette University
Enrico Prampolini, Marquette University
Abubakar Siddique, Marquette University
Henry P. Medeiros, Marquette UniversityFollow
Francesca Odone, University of Genoa

Document Type

Conference Proceeding

Publication Date

2019

Publisher

IEEE

Source Publication

2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)

Source ISSN

978-1-7281-2607-4

Abstract

In this paper, a novel method for tracker fusion is proposed and evaluated for vision-based object tracking. This work combines three distinct popular techniques into a recursive Bayesian estimation algorithm. First, a semi-supervised learning approach is used to train deep neural networks capable of detecting anomalous visual tracking behavior. Next, the network output is used to compute maximum a posteriori scores. Finally, these scores are integrated into the observation weighing mechanism of an existing data fusion algorithm. We evaluated the proposed algorithm on the OTB-100 benchmark dataset and compared its performance to the performance of the baseline fusion approach.

Comments

Accepted version. Published in the proceedings of the 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC): 501-506. DOI. © 2019 The Institute of Electrical and Electronics Engineers. Used with permission.

Recommended Citation

Reznichenko, Yevgeniy; Prampolini, Enrico; Siddique, Abubakar; Medeiros, Henry P.; and Odone, Francesca, "Visual Tracking with Autoencoder-Based Maximum A Posteriori Data Fusion" (2019). Electrical and Computer Engineering Faculty Research and Publications. 643.
https://epublications.marquette.edu/electric_fac/643