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In visual surveillance scenarios, stationary cameras are widely used.
Gait recognition can be used in visual surveillance, forensics, robotics, computer animation, etc.
In visual surveillance, this is equivalent to knowing which camera captured a video stream.
We provide a discussion on future research directions in human detection in visual surveillance.
In visual surveillance and satellite imaging systems, certain regions of interest in the input video must be magnified for more detailed analyses.
In visual surveillance of both humans and vehicles, a video stream is processed to characterize the events of interest through the detection of moving objects in each frame.
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Learning typical motion patterns from video scenes is important in automatic visual surveillance.
These are some of the reasons why face clustering remains extremely challenging in unconstrained visual surveillance situations.
One of the main challenges in any visual surveillance systems is to identify objects of interest from the background.
A person in a visual surveillance system can be identified using face recognition [85, 113 122] and gait recognition [123 131] techniques.
In uncontrolled visual surveillance scenes, the use of a face recognition system remains complicated due to the poor quality of face images.
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