Effective tracking of highly maneuvering objects while preserving robustness to illumination changes is a challenging problem. Conventionally the particle filter based color trackers (CPF) are efficiently used for non-linear estimation problems. Difficulties arise from the non-stationarity in lighting conditions through long video sequences that prevents efficient tracking of highly maneuvering objects. In order to improve the tracking performance we introduce a model that integrates the variable rate particle filtering (VRPF) into the CPF. The integrated tracking model called as variable rate color-based particle filtering (VRCPF) employs the non-uniform state update scheme of VRPF enhanced by an adaptive target update mechanism. Unlike the existing methods the VRCPF applies an exponential weighting scheme on the similarity metric between the target and candidate models and assigns the state points in such a way that allowing adaptive control of illumination changes in temporal domain. It is shown that although it relies on conventional color histograms, the VRCFP highly improves the maneuvering target tracking performance under severe illumination changes while provides comparable performance on BoBoT benchmarking dataset.