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Journal Articles Journal of Real-Time Image Processing Year : 2016

Fast prototyping of a SoC-based smart camera:a real-time fall detection case study

Abstract

Smart camera, i.e. cameras that are able to acquire and process images in real-time, is a typical example of the new embedded computer vision systems. A key example of application is automatic fall detection, which can be useful for helping elderly people in daily life. In this paper, we propose a methodology for development and fast-prototyping of a fall detection system based on such a smart camera, which allows to reduce the development time compared to standard approaches. Founded on a supervised classification approach, we propose a HW/SW implementation to detect falls in a home environment using a single camera and an optimized descriptor adapted to real-time tasks. This heterogeneous implementation is based on Xilinx’s system-on-chip named Zynq. The main contributions of this work are (i) the proposal of a codesign methodology. These methodologies enable the HW/SW partitioning to be delayed using high-level algorithmic description and high-level synthesis tools. Our approach enables fast prototyping which allows fast architecture exploration and optimisation to be performed, (ii) the design of a hardware accelerator dedicated to boostingbased classification, which is a very popular and efficient algorithm used in image analysis, (iii) the proposal of falldetection embedded in a smart camera and enabling integration into the elderly people environment. Performances of our system are finally compared to the state-of-the-art.
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Dates and versions

hal-01074358 , version 1 (14-10-2014)

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Benaoumeur Senouci, Imen Charfi, Heyrman Barthelemy, Julien Dubois, Johel Miteran. Fast prototyping of a SoC-based smart camera:a real-time fall detection case study. Journal of Real-Time Image Processing, 2016, 12 (4), pp.649-662. ⟨10.1007/s11554-014-0456-4⟩. ⟨hal-01074358⟩
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