Rough-Set-Theoretic Fuzzy Cues-Based Object Tracking Under Improved Particle Filter Framework

Object tracking from video sequences, although well researched, still faces difficulties under certain challenging situations. We propose a new particle filter-based tracking, where the fuzzy observation model exploits spatial correlation in a rough set-theoretic framework. We further improved certain critical steps of the particle filter such as mean state estimation and resampling. In our approach, mean state of the object at each time step is estimated with an adaptive number of high-weighted samples. These high-weighted samples are propagated along with the fresh samples generated around the current mean state to maintain sample diversity and avoid sample impoverishment. We later extended our approach to multi-cues. Given the wide applicability of statistical texture in various vision applications due to its robustness under various challenging situations, we propose to use fuzzy statistical texture cues along with fuzzy roughness color cues, and the weight for each cue is adaptively determined using the discriminating ability of the cue. Results show that our proposed particle filtering approach has less error variance across simulations and can perform tracking under challenging situations better than the contemporary ones.