Author: Shishir K. Shah
Abstract:
Providing automatic monitoring of surveillance video to help
security officers detect and predict suspicious activities is critical so that
they have enough time to take specific actions to prevent a crime or mitigate a
security threat.
The ultimate goal of this research is to develop an
intelligent, non-obtrusive, real-time, continuous monitoring system for
assessing activity and predicting suspicious and criminal behavior across a
network of distributed cameras.
To that end, the purpose of this project was to develop new
recognition algorithms that can track and identify human activities for wide
area video surveillance across large distributed camera networks. The developed
prototype has two main modular components: human detection and tracking, and
re-identification of body parts.
It is envisioned that the algorithms would support a
non-obtrusive system that could continuously track all objects across a network
of distributed cameras, analyze movements, and detect and measure descriptive
information continuously; and serve as a decision system that can correlate
each object’s patterns with others, generate models of suspicious/criminal
activity, and generate activity alerts for security personnel who monitor and
make critical decisions.
The findings indicate that the new tracking algorithms can
better understand the motion of humans dependent on their interactions with
both animate and inanimate objects. The research has successfully developed a
model for collecting, testing, and tracking these algorithms for use in a
distributed camera network.
Future work realized from the research is related to person
re-identification and the understanding of human behavior that can be modeled
to impact all aspects of wide area video surveillance.
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