  With rapidly increasing deployment of surveillance cameras, the reliable
methods for automatically analyzing the surveillance video and recognizing
special events are demanded by different practical applications. This paper
proposes a novel effective framework for security event analysis in
surveillance videos. First, convolutional neural network (CNN) framework is
used to detect objects of interest in the given videos. Second, the owners of
the objects are recognized and monitored in real-time as well. If anyone moves
any object, this person will be verified whether he/she is its owner. If not,
this event will be further analyzed and distinguished between two different
scenes: moving the object away or stealing it. To validate the proposed
approach, a new video dataset consisting of various scenarios is constructed
for more complex tasks. For comparison purpose, the experiments are also
carried out on the benchmark databases related to the task on abandoned luggage
detection. The experimental results show that the proposed approach outperforms
the state-of-the-art methods and effective in recognizing complex security
events.
