Basic Video-Surveillance with Low Computational and Power Requirements Using Long-Exposure Frames

Research in video surveillance is nowadays mainly directed towards improving reliability and gaining deeper levels of scene understanding. On the contrary, we take a different route and investigate a novel, unusual approach to a very simple surveillance task – activity detection – in scenarios where computational and energy resources are extremely limited, such as Camera Sensor Networks. Our proposal is based on shooting long-exposure frames, each covering a long period of time, thus enabling the use of frame rates even one order of magnitude slower than usual – which reduces computational costs by a comparable factor; however, as exposure time is increased, moving objects appear more and more transparent, and eventually become invisible in longer exposures. We investigate the consequent tradeoff, related algorithms and their experimental results with actual long-exposure images. Finally we discuss advantages (such as its intrinsic ability to deal with low-light conditions) and disadvantages of this approach.