  This paper aims at recognizing partially observed human actions in videos.
Action videos acquired in uncontrolled environments often contain corrupt
frames, which make actions partially observed. Furthermore, these frames can
last for arbitrary lengths of time and appear irregularly. They are
inconsistent with training data and degrade the performance of pre-trained
action recognition systems. We present an approach to address this issue. For
each training and testing actions, we divide it into segments and explore the
mutual dependency between temporal segments. This property states that the
similarity of two actions at one segment often implies their similarity at
another. We augment each segment with extra alternatives retrieved from
training data. The augmentation algorithm is designed in a way where a few
alternatives are good enough to replace the original segment where corrupt
frames occur. Our approach is developed upon hidden conditional random fields
and leverages the flexibility of hidden variables for uncertainty handling. It
turns out that our approach integrates corrupt segment detection and
alternative selection into the process of prediction, and can recognize
partially observed actions more accurately. It is evaluated on both fully
observed actions and partially observed ones with either synthetic or real
corrupt frames. The experimental results manifest its general applicability and
superior performance, especially when corrupt frames are present in the action
videos.
