Table 8.

Some GMM Optimization Schemes.

Reference	Optimization Method	Application	Comments
[106]	Minimization of Floating-Point Computations	Background Subtraction	The results of this research were impressive, showing no degradation in accuracy except for lower recall rates.
[107]	Comprehensive Sensing	Background Subtraction for real-time tracking in Embedded Vision	The results of this research reveal good performance for computational speed and reduce the memory footprint by 50%
[39]	Integer-based technique	Background/foreground Segmentation	This work shows good performance for processors without FPU, thus reducing computation cost and reducing the memory footprint to 1/12 of the original GMM; however, it cannot be adopted for models with more than two Gaussians