Abstract
Detection of the motion of foreground objects on the backdrop of constantly changing and complex visuals has always been challenging. The motion of foreground objects, which is termed as salient motion, is marked by its predictability compared to the more complex unpredictable motion of the backgrounds like fluttering of leaves, ripples in water, smoke filled environments etc. We introduce a novel approach to detect this salient motion based on the control theory concept of ’observability’ from the outputs, when the video sequence is represented as a linear dynamical system. The resulting algorithm is tested on a set of challenging sequences and compared to the state-of-the-art methods to showcase its superior performance on grounds of its computational efficiency and detection capability of the salient motion.
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Gopalakrishnan, V., Hu, Y., Rajan, D. (2011). Sustained Observability for Salient Motion Detection. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_57
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DOI: https://doi.org/10.1007/978-3-642-19318-7_57
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