TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object Tracking
<|reference_start|>TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object Tracking: There is currently strong interest in improving visual object tracking by augmenting the RGB modality with the output of a visual event camera that is particularly informative about the scene motion. However, existing approaches perform event feature extraction for RGB-E tracking using traditional appearance models, which have been optimised for RGB only tracking, without adapting it for the intrinsic characteristics of the event data. To address this problem, we propose an Event backbone (Pooler), designed to obtain a high-quality feature representation that is cognisant of the innate characteristics of the event data, namely its sparsity. In particular, Multi-Scale Pooling is introduced to capture all the motion feature trends within event data through the utilisation of diverse pooling kernel sizes. The association between the derived RGB and event representations is established by an innovative module performing adaptive Mutually Guided Fusion (MGF). Extensive experimental results show that our method significantly outperforms state-of-the-art trackers on two widely used RGB-E tracking datasets, including VisEvent and COESOT, where the precision and success rates on COESOT are improved by 4.9% and 5.2%, respectively. Our code will be available at https://github.com/SSSpc333/TENet.<|reference_end|>
@article{shao2024tenet:,
    title={TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling
  and Mutually-Guided Fusion for RGB-E Object Tracking},
    author={Pengcheng Shao, Tianyang Xu, Zhangyong Tang, Linze Li, Xiao-Jun Wu,
  Josef Kittler},
    journal={arXiv preprint arXiv:2405.05004},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05004},
    primaryClass={cs.CV}
}