  Particle Filter(PF) is used extensively for estimation of target Non-linear
and Non-gaussian state. However, its performance suffers due to inherent
problem of sample degeneracy and impoverishment. In order to address this, we
propose a novel resampling method based upon Crow Search Optimization to
overcome low performing particles detected as outlier. Proposed outlier
detection mechanism with transductive reliability achieve faster convergence of
proposed PF tracking framework. In addition, we present an adaptive fuzzy
fusion model to integrate multi-cue extracted for each evaluated particle.
Automatic boosting and suppression of particles using proposed fusion model not
only enhances performance of resampling method but also achieve optimal state
estimation. Performance of the proposed tracker is evaluated over 12 benchmark
video sequences and compared with state-of-the-art solutions. Qualitative and
quantitative results reveals that the proposed tracker not only outperforms
existing solutions but also efficiently handle various tracking challenges. On
average of outcome, we achieve CLE of 7.98 and F-measure of 0.734.
