Salient motion detection through state controllability

Salient motion detection is a challenging task especially when the motion is obscured by dynamic background motion. Salient motion is characterized by its consistency while the non-salient background motion typically consists of dynamic motion such as fog, waves, fire etc. In this paper, we present a novel framework for identifying salient motion by modelling the video sequence as a linear dynamic system and using controllability of states to estimate salient motion. The proposed saliency detection algorithm is tested on a challenging benchmark video dataset and the performance is compared with other state-of-the-art algorithms. The results of the comparison indicate that the proposed algorithm demonstrates superior performance when compared to other state-of-the-art methods and with higher computational efficiency.