  Most of the crowd abnormal event detection methods rely on complex
hand-crafted features to represent the crowd motion and appearance.
Convolutional Neural Networks (CNN) have shown to be a powerful tool with
excellent representational capacities, which can leverage the need for
hand-crafted features. In this paper, we show that keeping track of the changes
in the CNN feature across time can facilitate capturing the local abnormality.
We specifically propose a novel measure-based method which allows measuring the
local abnormality in a video by combining semantic information (inherited from
existing CNN models) with low-level Optical-Flow. One of the advantage of this
method is that it can be used without the fine-tuning costs. The proposed
method is validated on challenging abnormality detection datasets and the
results show the superiority of our method compared to the state-of-the-art
methods.
