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 o...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 ACMM-hased object detection shows very good performance with respect to object detection accuracy, AGMM is very complex model requiring lots of floatingpoint 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 rcal-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 nmnher and floatingpoint arithmetic in processing of AGMM algorithm. Experimental results shows that the proposed implementation have a high potential in real-time applications.展开更多
微处理器芯片的生态建设是高端装备与智能微系统自主、可控的关键,尽管国产数字信号处理(digital signal processing, DSP)器件及其相关开发应用技术近年来得到了一定的发展,但与需求仍存在较大差距。在主动噪声控制领域,前馈型多通道...微处理器芯片的生态建设是高端装备与智能微系统自主、可控的关键,尽管国产数字信号处理(digital signal processing, DSP)器件及其相关开发应用技术近年来得到了一定的发展,但与需求仍存在较大差距。在主动噪声控制领域,前馈型多通道控制方案比单通道有较大的控制范围和较好的性能,但对系统的运算能力有较高的要求。文章以多通道FxLMS算法为基础,对多通道降噪系统的运算量进行了分析,依据国产DSP开发板的电路结构,设计了控制系统方案,并进行了实验研究。实验表明,所设计的噪声控制系统运算效率较ARM作为运算器提高了80%,对100~1 000 Hz内的周期性噪声信号衰减达到15~20 dB,证明了该方案的正确性。展开更多
基金supported by Soongsil University Research Fund and BK 21 of Korea
文摘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 ACMM-hased object detection shows very good performance with respect to object detection accuracy, AGMM is very complex model requiring lots of floatingpoint 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 rcal-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 nmnher and floatingpoint arithmetic in processing of AGMM algorithm. Experimental results shows that the proposed implementation have a high potential in real-time applications.
