Optimal Metric Evaluation-Based Multicue Inverse Sparse Appearance Model for Object Tracking

In order to obtain the discriminative compact appearance model for tracking objects effectively, this paper proposes a new structural tracking strategy that includes multicue inverse sparse appearance model and optimal metric evaluation between online robust templates and a limited number of particle samples in the looping process. Multicue inverse sparse appearance model globally improves the efficient selection of informative particle samples that can avoid the cumbersome coding and decoding cost for the trivial random particle samples. Only the most potential crucial cases are involved in each tracking loop. This refrains from unreasonable, rough numerical reduction of particle samples and also keeps the unbiasedness and dynamic stochasticness of the sampling process. Meanwhile, low-rank self-representatives for positive and negative samples facilitate the formulation of a suitable code book that arranges the useful sparse coefficients for feature bags and facilitates optimal metric evaluation for online training. It also alleviates the accuracy degradation of tracking occluded objects and improves the robustness of the tracker. Both of them preserve the discriminative compactness of target which speeds up particle filtering localization to separate the target object from distractors. Moreover, the proposed method exploits online appearance representations to learn the sharing compact information that avoids massive calculation burdens for massive visual data.