Non-linear target trajectory prediction for robust visual tracking

The occlusion of the target in tracking usually causing failure that is a serious issue for long-term sequences. Though the siamese network-based trackers obtained considerable performance, it still suffers from the target missing issue when the target is obscured by the same semantic information interferent. To address this issue, a novel occlusion awareness algorithm is proposed, which can both address the occlusion issue and the same semantic information false identification issues. In addition, a novel generative adversarial training and long short term memory (LSTM) based target trajectory prediction algorithm is proposed to predict the possible direction of the target in the following frames. The proposed trajectory prediction algorithm can deal with complicated tracking situations more robustly than the traditional algorithms, e.g. Kalman filter. To further improve the occlusion awareness ability of the proposed algorithm, an occlusion supervision-based training strategy is proposed, which can improve the robustness of the occlusion awareness ability of the proposed occlusion awareness model. In addition, for accurate estimation of the target bounding box, a distance intersection over union (DIOU) loss for regression training is adopted. A comprehensive evaluation is performed on OTB2015, VOT2016, and VOT2018 to evaluate the effectiveness of the proposed algorithm. The experiment results demonstrate that the proposed algorithms perform well and can largely alleviate the tracking failure issue of the siamese network-based tracker caused by occlusion and the same semantic information target identification.