Automated Video Surveillance at Night

Period of Performance: 06/16/2003 - 04/15/2004

$99K

Phase 1 STTR

Recipient Firm

Perceptek
12395 North Mead Way
Littleton, CO 80125
Principal Investigator
Firm POC

Research Institution

University of Central Florida
4000 Central Florida Boulevard
Orlando, FL 32816
Institution POC

Abstract

Visual surveillance systems have proliferated to the point where security personnel are overwhelmed by the number of video feeds that need to be continuously monitored. And the monitoring task is made even more difficult at night since nighttime video data can be relatively noisy compared to daytime video. In addition, many sensors applicable for nighttime use do not provide the amount of information, color and texture for example, that is often available in daytime video. In order to assist security personnel monitoring a site at night, we propose an intelligent video analysis component that can be embedded within surveillance systems so that security personnel can be alerted when something of importance appears within the video. Working with the operator, this component will intelligently remove motion clutter, detect objects in disallowed areas, detect objects performing disallowed behaviors such as running, and detect people performing suspicious behaviors such as loitering. In order to deal with the characteristics of nighttime video, the proposed system will exploit motion within the video directly and use how an object is moving, rather than its structure or static appearance, in order to recognize the type of object and its behavior. Visual surveillance systems have been used for decades to deter and record crime and provide extra sets of eyes to security personnel. However, surveillance systems have proliferated to the point where security personnel are overwhelmed by the number of video feeds that need to be continuously monitored. Security personnel''s job is further complicated when monitoring an outdoor environment at night. The intelligent video analysis component that we are proposing will alleviate much of the tedium associated with monitoring video surveillance feeds and thereby make the operator and surveillance system more effective. The commercial success of surveillance systems is well established. However, the commercial viability of automated or partially automated surveillance systems has yet to be fully realized. The key to commercial success will lie with the ability to address a restricted, but useful set of monitoring tasks, be robust to the challenging characteristics of nighttime video, and exploit a human in the loop in the most beneficial and unobtrusive manner possible.