Title: The embedded real-time detection system of moving object based on improved Gaussian mixture model

Authors: Zhiwei Tang; Ziwei Lin; Bin Li; Longhu Chen

Addresses: School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China ' IOT Department, The Third Research Institute of Ministry of Pubic Security, Shanghai, China ' School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China ' IOT Department, The Third Research Institute of Ministry of Pubic Security, Shanghai, China

Abstract: Because of its complexity, the Gaussian mixture model algorithm costs many computing resources (CPU cycles) and it is difficult to realise real-time moving object detection on the embedded system. A real-time motion detection method based on an improved Gaussian mixture model is presented in this paper, which is optimised and adjusted for the Gaussian mixture model. It is also optimised on C language and CPU for running on an embedded system. The design of software and hardware on DM6446 is presented in detail including the implementation of the algorithm. The experimental results show that the system can effectively overcome the interference from external environment and realise multi-object real-time motion detection and tracking.

Keywords: Gaussian mixture model; DSP optimisation; digital signal processing; real-time detection; motion detection; embedded systems; moving objects; motion tracking.

DOI: 10.1504/IJES.2016.076105

International Journal of Embedded Systems, 2016 Vol.8 No.2/3, pp.119 - 124

Available online: 26 Apr 2016

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