Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal Mines

In view of the complex and changeable environment in underground coal mines, an improved algorithm based on the principal component analysis-scale invariant feature transform (PCA-SIFT) and mean shift is proposed to address the issues for which existing tracking algorithms are not adequate; for example, when differentiating between moving targets and the background, the tracking in the case of moving objects (e.g., confusion between foreground and background) is not optimal. This results in poor resolution and the inability to deal with very dusty conditions, scale change, and rotation. The proposed feature target tracking model was developed using the scale invariance property of the PCA-SIFT feature-extraction algorithm. Finally, the mean-shift method was used to track moving objects. The experimental results showed that the optimized algorithm for tracking moving objects was significantly better and more robust than the existing algorithm.