Fuzzy Kalman Filtering for 3-D Lagrangian Particle Tracking using Blob Detection

3-D Lagrangian Particle Tracking (3DLPT) is becoming widely used to characterize the convective indoor air movements in large scale spaces. The need to implement a robust algorithm led us to develop a multi-scale based approach to detect features (Helium filled soap bubbles). On the other hand, the particle tracking is another challenging problem. To this end, a new tracking algorithm based on fuzzy Kalman filtering is proposed in this paper. The Kalman filter is used to optimally estimate the new position of the particles based on their actual position. In our approach, the initial particle positions are represented with multivariate fuzzy sets.