Can Human Visual Surveillance be Improved
with Intent Recognition?
Alireza Tavakkoli, Donald Loffredo
In video surveillance applications, trained operators
watch a number of screens simultaneously to detect potential
security threats. Looking for such events in real time,
in multiple videos simultaneously, is cognitively challenging
for human operators. This study suggests that there is
a significant need to use an automated video analysis system
to aid human perception of security events in video
surveillance applications. In this paper the performance of
humans in observing a simulated environment is studied
and quantified. Furthermore, this paper proposes an automated
mechanism to detect events before they occur by
means of an automated intent recognition system. Upon
the detection of a potential event the proposed mechanism
communicates the location of such potential threat to the
human operator to redirect attention to the areas of interest
within the video. Studying the improvements achieved
by applying the intent recognition into the simulated video
surveillance application in a two phase trial supports the
need for an automated event detection approach in improving
human video surveillance performance. Moreover, this
paper presents a comparison of the performance in video
surveillance with and without the aid of the intent recognition
mechanism. Full Text
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