Delayed rectification of discriminative correlation filters for visual tracking

Discriminative correlation filters (DCF) have demonstrated competitive tracking performance in recent years. In these approaches, DCF methods only learn the appearance models with the historical tracking results, thus have the risks of drifting the targets due to the unforeseen target appearances in the future. In this paper, we present a novel tracking framework which rectifies the DCF models in the current frame with the potential future target appearances. To achieve this, the tracking model is updated with time-delay strategies and the model learning in each frame consists of two strategies: an exploration module and an exploitation module. The exploration module aims at discovering the potential target appearances in the near future, while the exploitation module further combines the future target appearances with the historical tracking results to learn more robust DCF models. To validate the proposed method, we integrate it into two state-of-the-art DCF trackers, i.e., spatially regularized discriminative correlation filters decontamination and efficient convolution operators, and also conduct extensive experiments on three tracking benchmarks: OTB-2015, Temple-Color and LaSOT. The results show that by incorporating with the proposed framework, the modified DCF methods can leverage the future target appearances for learning more robust models and are also superior to the baseline methods. In addition, they can also achieve competitive performance against the state-of-the-art methods on several datasets.