  Currently, the safety of people has become a very important problem in
different places including subway station, universities, colleges, airport,
shopping mall and square, city squares. Therefore, considering intelligence
event detection systems is more and urgently required. The event detection
method is developed to identify abnormal behavior intelligently, so public can
take action as soon as possible to prevent unwanted activities. The problem is
very challenging due to high crowd density in different areas. One of these
issues is occlusion due to which individual tracking and analysis becomes
impossible as shown in Fig. 1. Secondly, more challenging is the proper
representation of individual behavior in the crowd. We consider a novel method
to deal with these challenges. Considering the challenge of tracking, we
partition complete frame into smaller patches, and extract motion pattern to
demonstrate the motion in each individual patch. For this purpose, our work
takes into account KLT corners as consolidated features to describe moving
regions and track these features by considering optical flow method. To embed
motion patterns, we develop and consider the distribution of all motion
information in a patch as Gaussian distribution, and formulate parameters of
Gaussian model as our motion pattern descriptor.
