This paper tackles the problem of robust change detection in image sequences from static cameras. Motion cues are detected using frame differencing with an adaptive background estimation modelled by a mixture of Gaussians. Illumination invariance and elimination or detection of shadows is achieved by using a colour chromaticity representation of the image data.

This paper tackles the problem of robust change detection in image sequences from static cameras. Motion cues are detected using frame differencing with an adaptive background estimation modelled by a mixture of Gaussians. Illumination invariance and elimination or detection of shadows is achieved by using a colour chromaticity representation of the image data. The combination of the colourand ...

Abstract In modern era of aviation technology evolution, unmanned aerial vehicles have proved to be crucial in all fields including military and research. The development robust control system successful mission accomplishment requires an meticulous UAV model. aim this paper is lay out elaborate model estimation scheme using various structure techniques Auto-regressive Exogenous, Moving Average...

The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which later declared as a global pandemic view its exponential rise and spread around the world. Resultantly, scientific medical research communities globe geared up to curb spread. In this manuscript, authors claim competence AI-mediated methods predict mortality rate. Efficient prediction model enables he...

stochastic, processes can be stationary or nonstationary. they depend on the magnitude of shocks. in other words, in an auto regressive model of order one, the estimated coefficient is not constant. another finding of this paper is the relation between estimated coefficients and residuals. we also develop a catastrophe and chaos theory for change of roots from stationary to a nonstationary one ...

ÐStandard, exact techniques based on likelihood maximization are available for learning Auto-Regressive Process models of dynamical processes. The uncertainty of observations obtained from real sensors means that dynamics can be observed only approximately. Learning can still be achieved via aEM-KoÐExpectation-Maximization (EM) based on Kalman Filtering. This cannot handle more complex dynamics...