Abstract
RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role to represent the target appearance in RGBT tracking. In this paper, we propose a novel challenge-aware neural network to handle the modality-shared challenges (e.g., fast motion, scale variation and occlusion) and the modality-specific ones (e.g., illumination variation and thermal crossover) for RGBT tracking. In particular, we design several parameter-shared branches in each layer to model the target appearance under the modality-shared challenges, and several parameter-independent branches under the modality-specific ones. Based on the observation that the modality-specific cues of different modalities usually contains the complementary advantages, we propose a guidance module to transfer discriminative features from one modality to another one, which could enhance the discriminative ability of some weak modality. Moreover, all branches are aggregated together in an adaptive manner and parallel embedded in the backbone network to efficiently form more discriminative target representations. These challenge-aware branches are able to model the target appearance under certain challenges so that the target representations can be learnt by a few parameters even in the situation of insufficient training data. From the experimental results we will show that our method operates at a real-time speed while performing well against the state-of-the-art methods on three benchmark datasets.
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References
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. arXiv preprint arXiv:1405.3531 (2014)
Gao, Y., Li, C., Zhu, Y., Tang, J., He, T., Wang, F.: Deep adaptive fusion network for high performance rgbt tracking. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)
Jung, I., Son, J., Baek, M., Han, B.: Real-time mdnet. In: Proceedings of the European Conference on Computer Vision (2018)
Li, C., Cheng, H., Hu, S., Liu, X., Tang, J., Lin, L.: Learning collaborative sparse representation for grayscale-thermal tracking. IEEE Trans. Image Process. 25(12), 5743–5756 (2016)
Li, C., Liang, X., Lu, Y., Zhao, N., Tang, J.: Rgb-t object tracking: benchmark and baseline. Pattern Recogn. 96, 106977 (2019)
Li, C., Lu, A., Zheng, A., Tu, Z., Tang, J.: Multi-adapter rgbt tracking. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)
Li, C., Wu, X., Zhao, N., Cao, X., Tang, J.: Fusing two-stream convolutional neural networks for rgb-t object tracking. Neurocomputing 281, 78–85 (2018)
Li, C., Zhao, N., Lu, Y., Zhu, C., Tang, J.: Weighted sparse representation regularized graph learning for rgb-t object tracking. In: Proceedings of ACM International Conference on Multimedia (2017)
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Perez, E., Strub, F., De Vries, H., Dumoulin, V., Courville, A.: Film: visual reasoning with a general conditioning layer. In: Proceedings of the AAAI Conference on Artificial Intelligence (2018)
Qi, Y., Zhang, S., Zhang, W., Su, L., Huang, Q., Yang, M.H.: Learning attribute-specific representations for visual tracking. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019)
Rebuffi, S.A., Bilen, H., Vedaldi, A.: Learning multiple visual domains with residual adapters. In: Proceedings of the Advances in Neural Information Processing Systems (2017)
Rebuffi, S.A., Bilen, H., Vedaldi, A.: Efficient parametrization of multi-domain deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Wang, X., Yu, K., Dong, C., Loy, C.C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2018)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
Zhang, L., Danelljan, M., Gonzalez-Garcia, A., van de Weijer, J., Shahbaz Khan, F.: Multi-modal fusion for end-to-end rgb-t tracking. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)
Zhu, Y., Li, C., Luo, B., Tang, J., Wang, X.: Dense feature aggregation and pruning for rgbt tracking. In: Proceedings of the ACM International Conference on Multimedia (2019)
Acknowledgement
This work was supported in part by the Major Project for New Generation of AI under Grant 2018AAA0100400, in part by the National Natural Science Foundation of China under Grant 61702002 and Grant 61976003, and in part by the Key Project of Research and Development of Anhui Province under Grant 201904b11020037.
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Li, C., Liu, L., Lu, A., Ji, Q., Tang, J. (2020). Challenge-Aware RGBT Tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12367. Springer, Cham. https://doi.org/10.1007/978-3-030-58542-6_14
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DOI: https://doi.org/10.1007/978-3-030-58542-6_14
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