  Crowd flow segmentation is an important step in many video surveillance
tasks. In this work, we propose an algorithm for segmenting flows in H.264
compressed videos in a completely unsupervised manner. Our algorithm works on
motion vectors which can be obtained by partially decoding the compressed video
without extracting any additional features. Our approach is based on modelling
the motion vector field as a Conditional Random Field (CRF) and obtaining
oriented motion segments by finding the optimal labelling which minimises the
global energy of CRF. These oriented motion segments are recursively merged
based on gradient across their boundaries to obtain the final flow segments.
This work in compressed domain can be easily extended to pixel domain by
substituting motion vectors with motion based features like optical flow. The
proposed algorithm is experimentally evaluated on a standard crowd flow dataset
and its superior performance in both accuracy and computational time are
demonstrated through quantitative results.
