  Video object segmentation can be considered as one of the most challenging
computer vision problems. Indeed, so far, no existing solution is able to
effectively deal with the peculiarities of real-world videos, especially in
cases of articulated motion and object occlusions; limitations that appear more
evident when we compare their performance with the human one. However, manually
segmenting objects in videos is largely impractical as it requires a lot of
human time and concentration. To address this problem, in this paper we propose
an interactive video object segmentation method, which exploits, on one hand,
the capability of humans to identify correctly objects in visual scenes, and on
the other hand, the collective human brainpower to solve challenging tasks. In
particular, our method relies on a web game to collect human inputs on object
locations, followed by an accurate segmentation phase achieved by optimizing an
energy function encoding spatial and temporal constraints between object
regions as well as human-provided input. Performance analysis carried out on
challenging video datasets with some users playing the game demonstrated that
our method shows a better trade-off between annotation times and segmentation
accuracy than interactive video annotation and automated video object
segmentation approaches.
