Human Motion Tracking Based on Adaptive Template Matching and GM(1,1)

Abstract

To solve the defect of poor robustness and realtimeliness of traditional correlation matching algorithm, this paper proposes a novel method that combines normalized autocorrelation matching algorithm, template updating strategy and grey forecasting model GM(1,1) with priority of new information. The real-time updating of the template image size improves the deformation resistance capability of the autocorrelation matching algorithm, and assignment of great weight to new information improves the robustness of the grey model. The use of forecasting increases the speed of the adaptive template and improves the real-timeliness of the algorithm. Meanwhile, the results of template matching provide the GM(1,1) model with new data for forecasting the target position of the next frame of image. When human motion region is shaded, the forecast value is used to replace the real value to continue the motion and thus to improve the real-timeliness and robustness of the system.

Video Shot-Boundary Detection based on Matrix sequence Grey model

“…(1) Image processing, for example, image object tracking, image edge extraction, image compression and image denoising. Self-adapting template match and grey model were used by Fang to track the move of people [2]. Combing with Mean shift and GM(1,1) model, multi-feature space information is used to track video object [3].…”

Strengthening barrier-coverage of static sensor network with mobile sensor nodes

“…In particular, we adopt a mathematical model known as a single variable first-order grey model, GM(1,1), which has been widely used to predict events which are repeatedly occurring and is known to be highly reliable and efficient for this purpose [10][11][12][13][14]. (b) Once we identify static sensor nodes which have higher chance to detect intruders, we relocate the available mobile sensor nodes nearby the static sensors so that the area covered by these nodes can be monitored even more thoroughly.…”