  Object tracking is one of the fundamental problems in visual recognition
tasks and has achieved significant improvements in recent years. The
achievements often come with the price of enormous hardware consumption and
expensive labor effort for consecutive labeling. A missing ingredient for
robust tracking is achieving performance with minimal modification on network
structure and semi-supervised learning intermittent labeled frames. In this
paper, we ad-dress these problems in a Bayesian tracking and detection
framework parameterized by neural network outputs. In our framework, the
tracking and detection process is formulated in a probabilistic way as
multi-objects dynamics and network detection uncertainties. With our
formulation, we propose a particle filter-based approximate sampling algorithm
for tracking object state estimation. Based on our particle filter inference
algorithm, a semi-supervised learn-ing algorithm is utilized for learning
tracking network on intermittent labeled frames by variational inference. In
our experiments, we provide both mAP and probability-based detection
measurements for comparison between our algorithm with non-Bayesian solutions.
We also train a semi-supervised tracking network on M2Cai16-Tool-Locations
Dataset and compare our results with supervised learning on fully labeled
frames.
