Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance,HCI,object-based video compression,etc.One of the most successful moving object detection algorithms is based on Adaptive Gaussian Mixture Model（AGMM）.Although AGMM-based object detection shows very good performance with respect to object detection accuracy,AGMM is very complex model requiring lots of floating-point arithmetic so that it should pay for expensive computational cost.Thus,direct implementation of the AGMM-based object detection for embedded DSPs without floating-point arithmetic HW support cannot satisfy the real-time processing requirement.This paper presents a novel real-time implementation of adaptive Gaussian mixture model-based moving object detection algorithm for fixed-point DSPs.In the proposed implementation,in addition to changes of data types into fixed-point ones,magnification of the Gaussian distribution technique is introduced so that the integer and fixed-point arithmetic can be easily and consistently utilized instead of real number and floating-point arithmetic in processing of AGMM algorithm.Experimental results shows that the proposed implementation have a high potential in real-time applications.
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