Multiple Instance Models Regression for Robust Visual Tracking

In comparison to single-model based trackers, the model-ensembled tracking strategy has shown a substantial adaptivity in handling various tracking challenges. As the performance of the tracker has been improved by combining different model outputs linearly, the insufficient consideration of each ensemble member's contribution still limits the tracking performance to be further enhanced. As the performance of the tracker has been improved by combining different model outputs linearly, the insufficient consideration of each ensemble member's contribution still limits the tracking performance to be further enhanced. In this paper, a tracking strategy based on multiple instance models regression (MIMRT) is proposed with a unified ensembling scheme. By formulating the tracking initialization with an instance model, the encoding process for an object's specific detail is performed corresponding to the samples in each frame. The advantage of this operation is to guarantee the model frame-wise discrimination of short-Term training, as well as to evaluate the reliability of each instance model by utilizing the long-lifetime samples obtained throughout the whole tracking procedure. To finalize the proposed tracking, all the independent instance models attached to the learned regression coefficients are ensembled with respect to the long-lifetime samples. This also effectively bridges the instance model as a latent variable to investigate a semantic association between the tracking model and the overall samples. A comprehensive experiment has shown that the proposed tracker is able to achieve superior performances compared to the state-of-Art tracking approaches on both short-Term datasets (e.g., OTB2013, OTB100, VOT2016, UAV123) and long-Term dataset (UAV20L).