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Visual attention based surveillance videos compression

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Visual attention models (VAM) try to mimic the human visual system in distinguishing salient regions from non-salient ones in the scene. Only a few attention models propose to detect salient motion in surveillance videos. These model utilizes static features such as color, intensity, orientation, face, and dynamic features such as motion to detect most salient regions in videos. This motivated us to propose a compression algorithm based on visual attention model that is developed specificly for surveillance videos. In this paper we are using a state of the art visual attention model developed by combining bottom-up, top-down, and motion cues. Based on its similarity with experimentally obtained gaze maps evaluated both visually and with quantitative measures, a compression model based on this attention model is proposed for H.264/AVC encoded videos. Our experimental results show that we can encode videos with same or better quality than those obtained with the standard baseline profile of the JM 18.0 reference encoder, while reducing the file size uptil 22%.
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Document Type: Research Article

Publication date: January 1, 2012

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  • CIC is the premier annual technical gathering for scientists, technologists, and engineers working in the areas of color science and systems, and their application to color imaging. Participants represent disciplines ranging from psychophysics, optical physics, image processing, color science to graphic arts, systems engineering, and hardware and software development. While a broad mix of professional interests is the hallmark of these conferences, the focus is color. CICs traditionally offer two days of short courses followed by three days of technical sessions that include three keynotes, an evening lecture, a vibrant interactive (poster) papers session, and workshops. An endearing symbol of the meeting is the Cactus Award, given each year to the author(s) of the best interactive paper; there are also Best Paper and Best Student Paper awards.

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