Authors: Ming-Ching Chang, Jixu Chen, Siwei Lyu, Peter Tu
Abstract:
The purpose of this research is to advance the field of
video surveillance as it pertains to a variety of law enforcement needs.
This research focuses on the development of three new video
analytics technologies:
1. 3D Video
Representation and Event Summarization Front-end: which would allow users to
view 3D interactive events at a user-specific angle, with highlights, and in a
greater context.
2. One-shot
Learning for Action Recognition: which could allow users to recognize behaviors
such as gaze directions, expressions, and motion fields of a person.
3. Person
Specific Face Recognition: which would further improve recognition accuracy by
distinguishing features that are specific to a person of interest, such as
visible scars and hair styles.
The findings suggest that 3D video analytics technologies
are effective and could advance video surveillance in law enforcement by
providing a new form of recognition at a glance, allowing for more complex
multi-camera imagery.
The study found that this new form of event recognition,
using analytics engines, will be able to detect new types of behaviors with as
few as a single example. In addition, by considering person specific cues such
as hair styles and facial markings, it will become possible to more accurately
detect specific persons of interest.
The research implications of this work indicate that
surveillance systems of the future will be able to keep pace with the
ever-evolving demands on law enforcement.
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