UAV tracking is usually challenged by the dual-dynamic disturbances that arise from not only diverse moving target but also motion camera, leading to a more serious model drift issue than traditional visual tracking. In this work, we propose to alleviate this issue with distance-injected overlap maximization. Our idea is improving the accuracy of target localization by deriving a conceptually simple target localization loss and a global feature recalibration scheme in a mutual reinforced way. In particular, the target localization loss is designed by simply incorporating the normalized distance of target offset and generic semantic IoU loss, resulting in the distance-injected semantic IoU loss, and its minimal solution can alleviate the drift problem caused by camera motion. Moreover, the deep feature extractor is reconstructed and alternated with a feature recalibration network, which can leverage the global information to recalibrate significant features and suppress negligible features. Following by multi-scale feature concat, the proposed tracker can improve the discriminative capability of feature representation for UAV targets on the fly. Extensive experimental results on four benchmarks, i.e. UAV123, UAVDT, DTB70, and VisDrone, demonstrate the superiority of the proposed tracker against existing state-of-the-arts on UAV tracking.

            
@inproceedings{Chunhui2020ACMMM,
  author = {Chunhui Zhang, and Shiming Ge, and Kangkai Zhang, and Dan Zeng},
  title = {Accurate UAV Tracking with Distance-Injected Overlap Maximization},
  booktitle = {Proceedings of the 28th ACM International Conference on Multimedia},
  pages={565–573},
  year = {2020},
}         

        

Recent deep trackers have shown superior performance in visual tracking. In this article, we propose a cascaded correlation refinement approach to facilitate the robustness of deep tracking. The core idea is to address accurate target localization and reliable model update in a collaborative way. To this end, our approach cascades multiple stages of correlation refinement to progressively refine target localization. Thus, the localized object could be used to learn an accurate on-the-fly model for improving the reliability of model update. Meanwhile, we introduce an explicit measure to identify the tracking failure and then leverage a simple yet effective look-back scheme to adaptively incorporate the initial model and on-the-fly model to update the tracking model. As a result, the tracking model can be used to localize the target more accurately. Extensive experiments on OTB2013, OTB2015, VOT2016, VOT2018, UAV123, and GOT10k demonstrate that the proposed tracker achieves the best robustness against the state of the arts.

            
@ARTICLE{9069312,
    author={S. {Ge} and C. {Zhang} and S. {Li} and D. {Zeng} and D. {Tao}},
    journal={IEEE Transactions on Neural Networks and Learning Systems}, 
    title={Cascaded Correlation Refinement for Robust Deep Tracking}, 
    year={2021},
    volume={32},
    number={3},
    pages={1276-1288},
    doi={10.1109/TNNLS.2020.2984256}
}

        

Deep trackers have proven success in visual tracking. Typically, these trackers employ optimally pre-trained deep networks to represent all diverse objects with multi-channel features from some fixed layers. The deep networks employed are usually trained to extract rich knowledge from massive data used in object classification and so they are capable to represent generic objects very well. However, these networks are too complex to represent a specific moving object, leading to poor generalization as well as high computational and memory costs. This paper presents a novel and general framework termed channel distillation to facilitate deep trackers. To validate the effectiveness of channel distillation, we take discriminative correlation filter (DCF) and ECO for example. We demonstrate that an integrated formulation can turn feature compression, response map generation, and model update into a unified energy minimization problem to adaptively select informative feature channels that improve the efficacy of tracking moving objects on the fly. Channel distillation can accurately extract good channels, alleviating the influence of noisy channels and generally reducing the number of channels, as well as adaptively generalizing to different channels and networks. The resulting deep tracker is accurate, fast, and has low memory requirements. Extensive experimental evaluations on popular benchmarks clearly demonstrate the effectiveness and generalizability of our framework.

            
@ARTICLE{8891903,
  author={S. {Ge} and Z. {Luo} and C. {Zhang} and Y. {Hua} and D. {Tao}},
  journal={IEEE Transactions on Image Processing}, 
  title={Distilling Channels for Efficient Deep Tracking}, 
  year={2020},
  volume={29},
  number={},
  pages={2610-2621},
}

        

Recently, deep trackers have proven success in visual tracking due to their powerful feature representation. Among them, discriminative correlation filter (DCF) paradigm is widely used. However, these trackers are still difficult to learn an adaptive appearance model of the object due to the limited data available. To address that, this paper proposes a two-step augmentation discriminative correlation filters (TADCF) approach to improve robustness. Firstly, we propose an online frame augmentation scheme to obtain rich and robust deep features which can effectively alleviate background distractors, leading to better generalization and adaptation of the learned model. Secondly, an object augmentation mechanism is implemented by exploiting rotation continuity restriction, which simultaneously models target appearance changes from rotation and scale variations. Extensive experiments on four benchmarks illustrate that the proposed approach performs favorably against state-of-the-art trackers.

            
@INPROCEEDINGS{8785041,
  author={C. {Zhang} and S. {Ge} and Y. {Hua} and D. {Zeng}},
  booktitle={2019 IEEE International Conference on Multimedia and Expo (ICME)}, 
  title={Robust Deep Tracking with Two-step Augmentation Discriminative Correlation Filters}, 
  year={2019},
  volume={},
  number={},
  pages={1774-1779},
}