Effective tracking is still a big challenge due to lack of robust descriptors which captures discriminative features in non-controlled environment. We propose a novel descriptor based on Gabor wavelet and Partial Least Squares (PLS) discriminant analysis. Multi scale and multi orientation Gabor wavelets can extract selective local frequencies effectively in spatial and frequency domain. Due to the large dimension of feature vectors, dimensionality reduction is done using class aware PLS analysis. Unlike unsupervised Principal Component Analysis (PCA), PLS based subspace model learns target effectively by explicitly knowing the class labels of target and background region feature vectors. Tracking is done using particle filter and similarity between target and candidates is measured using low dimensional subspace model. To combat the target changes during tracking, novel static and dynamic target as well as background update strategy is used. Experimental results of various dataset demonstrate that the proposed tracker improves robustness and accuracy against representative trackers. © 2013 The Authors. Published by Elsevier Ltd.