Visual tracking with autoencoder-based maximum a posteriori data fusion

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.