  In this paper the problem of complex event detection in the continuous domain
(i.e. events with unknown starting and ending locations) is addressed. Existing
event detection methods are limited to features that are extracted from the
local spatial or spatio-temporal patches from the videos. However, this makes
the model vulnerable to the events with similar concepts e.g. "Open drawer" and
"Open cupboard". In this work, in order to address the aforementioned
limitations we present a novel model based on the combination of semantic and
temporal features extracted from video frames. We train a max-margin classifier
on top of the extracted features in an adaptive framework that is able to
detect the events with unknown starting and ending locations. Our model is
based on the Bidirectional Region Neural Network and large margin Structural
Output SVM. The generality of our model allows it to be simply applied to
different labeled and unlabeled datasets. We finally test our algorithm on
three challenging datasets, "UCF 101-Action Recognition", "MPII Cooking
Activities" and "Hollywood", and we report state-of-the-art performance.
