We present results on the PETS 2009 dataset using
surveillance systems based on holistic properties of the
video. In particular, we evaluate a crowd counting system,
based on regression of holistic (global) features, on the
PETS 2009 dataset. We also present experimental results
on crowd event detection when using the dynamic texture
model to represent holistic motion flow in the video