In this paper, we propose a novel and practical system for robust online face tracking in surveillance videos. The proposed system has two contributions: 1) sustained high performance for long-term tracking even when faces come in and out of the view frequently, and 2) extremely low complexity which allows for real-time deployment on various platforms. These advantages are achieved by designing a regular update framework based on a state-of-the-art face detector and a new histogram-assisted KLT (HAKLT) tracker. Experimental results demonstrate a superior and super-fast (>100fps) practical face tracking system.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we propose a novel and practical system for robust online face tracking in surveillance videos. The proposed system has two contributions: 1) sustained high performance for long-term tracking even when faces come in and out of the view frequently, and 2) extremely low complexity which allows for real-time deployment on various platforms. 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