ANOMALY DETECTION IN VIDEO USING KNOWLEDGE DRIVEN COMPUTATIONAL VISUAL ATTENTION MODEL
October 19, 2016
Visual attention models significantly reduce the computational complexity involved in anomaly detection. Top down cues such as knowledge about certain characteristics of an object can further decrease the computation time in a purely bottom up visual attention system. The proposed model was tested on several video sequences successfully and was found robust to noise and illumination variations.