  Vision-based action recognition is one of the most challenging research
topics of computer vision and pattern recognition. A specific application of
it, namely, detecting fights from surveillance cameras in public areas,
prisons, etc., is desired to quickly get under control these violent incidents.
This paper addresses this research problem and explores LSTM-based approaches
to solve it. Moreover, the attention layer is also utilized. Besides, a new
dataset is collected, which consists of fight scenes from surveillance camera
videos available at YouTube. This dataset is made publicly available. From the
extensive experiments conducted on Hockey Fight, Peliculas, and the newly
collected fight datasets, it is observed that the proposed approach, which
integrates Xception model, Bi-LSTM, and attention, improves the
state-of-the-art accuracy for fight scene classification.
