  Autonomous camera systems are often subjected to an optimization/filtering
operation to smoothen and stabilize the rough trajectory estimates. Most common
filtering techniques do reduce the irregularities in data; however, they fail
to mimic the behavior of a human cameraman. Global filtering methods modeling
human camera operators have been successful; however, they are limited to
offline settings. In this paper, we propose two online filtering methods called
Cinefilters, which produce smooth camera trajectories that are motivated by
cinematographic principles. The first filter (CineConvex) uses a sliding
window-based convex optimization formulation, and the second (CineCNN) is a CNN
based encoder-decoder model. We evaluate the proposed filters in two different
settings, namely a basketball dataset and a stage performance dataset. Our
models outperform previous methods and baselines on both quantitative and
qualitative metrics. The CineConvex and CineCNN filters operate at about 250fps
and 1000fps, respectively, with a minor latency (half a second), making them
apt for a variety of real-time applications.
