Underwater Target Tracking Based on Uncertainty-Inspired Image Enhancement

doi:10.19678/j.issn.1000-3428.0069724

Abstract

Abstract:

The task of Underwater Visual Object Tracking (UVOT) not only requires dealing with the common challenges in outdoor tracking but also faces many unique difficulties specific to the underwater environment, including but are not limited to, optical degradation and scattering, uneven illumination, low visibility, and hydrodynamics. In these scenarios, directly applying a large number of traditional outdoor scene object tracking methods directly to underwater scenes inevitably leads to performance degradation. To address the above issues, first, an Underwater Image Enhancement (UIE) module inspired by uncertainty is introduced, aimed at specifically improving the quality of underwater images. This method decomposes UIE into distribution estimation and consensus processes and introduces a new probability network to learn the enhancement distribution of underwater images, thereby addressing the bias problem in reference images. These are subsequently applied to an attention-based feature fusion network to propose a target tracking algorithm, called UTransT. The feature fusion network combines self- and cross-attention mechanisms to effectively fuse template and search region features. The experimental results show that on the UTB180 dataset, the success rate of UTransT is 0.8 percentage points higher than that of MixFormer, with the best performance in the comparison algorithm, and normalization accuracy is nearly 1.9 percentage points higher. On the VMAT dataset, the success rate is 1.2 percentage points higher than that of the best-performing Masked Appearance Transfer (MAT) algorithm, with 1.5 percentage points higher normalization accuracy. Moreover, UTransT facilitates real-time tracking at 65 frames per second. These experimental results validate the effectiveness and feasibility of the proposed algorithm in underwater object tracking tasks.

Key words: image enhancement, underwater target tracking, attention mechanism, distribution estimation, probabilistic network

摘要：

水下视觉目标跟踪(UVOT)任务不仅需要应对常见的露天跟踪挑战, 而且还需要面对水下环境所特有的诸多挑战, 包括但不限于光学退化和散射、光照不均、能见度低、水动力学等影响。在这种情况下, 直接将大量传统的露天场景目标跟踪方法应用于水下场景, 其性能下降是难以避免的。为了解决上述问题, 首先引入一种基于不确定性启发的水下图像增强(UIE)模块, 将UIE拆分为分布估计和共识过程, 并利用一种新的概率网络来学习水下图像的增强分布, 以解决参考图像的偏差问题。然后将UIE模块应用于基于注意力的特征融合网络, 提出水下目标跟踪算法UTransT, 其中的特征融合网络结合自注意力和交叉注意力机制, 以便有效地融合模板特征和搜索区域特征。实验结果表明: 在UTB180数据集上, UTransT的成功率相比于对比算法中表现最优的MixFormer提高了0.8百分点、归一化精度提高了1.9百分点; 在VMAT数据集上, 其成功率相比对比算法中表现最优的掩盖外观转移(MAT)算法提高了1.2百分点、归一化精度提高了1.5百分点; UTransT能够以65帧/s的速度进行实时跟踪。这验证了所提算法在改善水下目标跟踪任务中的有效性和可行性。

关键词: 图像增强, 水下目标跟踪, 注意力机制, 分布估计, 概率网络

LUO Xudong, YUAN Di, CHANG Xiaojun, HE Zhenyu. Underwater Target Tracking Based on Uncertainty-Inspired Image Enhancement[J]. Computer Engineering, 2025, 51(1): 11-19.

罗旭东, 袁笛, 常晓军, 何震宇. 基于不确定性启发图像增强的水下目标跟踪[J]. 计算机工程, 2025, 51(1): 11-19.

/ Recommend / Download Citations

URL: https://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0069724

https://www.ecice06.com/EN/Y2025/V51/I1/11

Figures/Tables 7

Fig.1 Overall framework of UTransT algorithm

Fig.2 ECCF module

Fig.3 Comparison of tracking results of different algorithms

Fig.4 Comparison of average overlap degree of different algorithms on UTB180 dataset

Fig.5 Performance comparison results on VMAT datasets

References 41

1	李珑, 刘凯, 李玲. 基于目标检测的时空上下文跟踪算法. 计算机工程, 2018, 44 (9): 263-268, 273. URL
	LI L , LIU K , LI L . Spatial-temporal context tracking algorithm based on target detection. Computer Engineering, 2018, 44 (9): 263-268, 273. URL
2	王春雷, 张建林, 李美惠, 等. 结合卷积Transformer的目标跟踪算法. 计算机工程, 2023, 49 (4): 281-288, 296. URL
	WANG C L , ZHANG J L , LI M H , et al. Object tracking algorithmcombining convolution and Transformer. Computer Engineering, 2023, 49 (4): 281-288, 296. URL
3	任立成, 杨嘉棋, 魏宇星, 等. 基于特征融合与双模板嵌套更新的孪生网络跟踪算法. 计算机工程, 2021, 47 (7): 239- 248. URL
	REN L C , YANG J Q , WEI Y X , et al. Tracking algorithm using Siamese network based on feature fusion and dual-template nested update. Computer Engineering, 2021, 47 (7): 239- 248. URL
4	FAN H , BAI H X , LIN L T , et al. LaSOT: a high-quality large-scale single object tracking benchmark. International Journal of Computer Vision, 2021, 129 (2): 439- 461. doi: 10.1007/s11263-020-01387-y
5	MÜLLER M, BIBI A, GIANCOLA S, et al. TrackingNet: a large-scale dataset and benchmark for object tracking in the wild[C]//Proceedings of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 310-327.
6	HUANG L H , ZHAO X , HUANG K Q . GOT-10k: a large high-diversity benchmark for generic object tracking in the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43 (5): 1562- 1577. doi: 10.1109/TPAMI.2019.2957464
7	ALAWODE B, DHAREJO F A, UMMAR M, et al. Improving underwater visual tracking with a large scale dataset and image enhancement[EB/OL]. [2024-03-11]. http://arxiv.org/abs/2308.15816.
8	ALAWODE B, GUO Y H, UMMAR M, et al. UTB180: a high-quality benchmark for underwater tracking[C]//Proceedings of Asian Conference on Computer Vision. Berlin, Germany: Springer, 2023: 442-458.
9	PANETTA K , KEZEBOU L , OLUDARE V , et al. Comprehensive underwater object tracking benchmark dataset and underwater image enhancement with GAN. IEEE Journal of Oceanic Engineering, 2022, 47 (1): 59- 75. doi: 10.1109/JOE.2021.3086907
10	CAI L , MCGUIRE N E , HANLON R , et al. Semi-supervised visual tracking of marine animals using autonomous underwater vehicles. International Journal of Computer Vision, 2023, 131 (6): 1406- 1427. doi: 10.1007/s11263-023-01762-5
11	GONZÁLEZ-SABBAGH S P , ROBLES-KELLY A . A survey on underwatercomputer vision. ACM Computing Surveys, 2023, 55 (13): 1- 39.
12	AKKAYNAK D, TREIBITZ T. Sea-Thru: a method for removing water from underwater images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2019: 1682-1691.
13	MANDAL A, PRAKASH M, BRINDHA T V, et al. Computer vision and deep learning for fish classification in underwater habitats[C]//Proceedings of the International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT). Washington D.C., USA: IEEE Press, 2023: 1-7.
14	TIAN C W , XIAO J Y , ZHANG B , et al. A self-supervised network for image denoising and watermark removal. Neural Networks, 2024, 174, 106218. doi: 10.1016/j.neunet.2024.106218
15	TIAN C W , YUAN Y X , ZHANG S C , et al. Image super-resolution with an enhanced group convolutional neural network. Neural Networks, 2022, 153, 373- 385. doi: 10.1016/j.neunet.2022.06.009
16	SETIAWAN A W, MENGKO T R, SANTOSO O S, et al. Color retinal image enhancement using CLAHE[C]//Proceedings of the International Conference on ICT for Smart Society. Washington D.C., USA: IEEE Press, 2013: 1-7.
17	LIU Y C , CHAN W H , CHEN Y Q . Automatic white balance for digital still camera. IEEE Transactions on Consumer Electronics, 1995, 41 (3): 460- 466. doi: 10.1109/30.468045
18	GALDRAN A , PARDO D , PICÓN A , et al. Automatic red-channel underwater image restoration. Journal of Visual Communication and Image Representation, 2015, 26, 132- 145. doi: 10.1016/j.jvcir.2014.11.006
19	PENG Y T , COSMAN P C . Underwater image restoration based on image blurriness and light absorption. IEEE Transactions on Image Processing, 2017, 26 (4): 1579- 1594. doi: 10.1109/TIP.2017.2663846
20	PENG Y T , CAO K M , COSMAN P C . Generalization of the dark channel prior for single image restoration. IEEE Transactions on Image Processing, 2018, 27 (6): 2856- 2868. doi: 10.1109/TIP.2018.2813092
21	LI J , SKINNER K A , EUSTICE R M , et al. WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robotics and Automation Letters, 2018, 3 (1): 387- 394.
22	LI C Y , GUO J C , GUO C L . Emerging from water: underwater image color correction based on weakly supervised color transfer. IEEE Signal Processing Letters, 2018, 25 (3): 323- 327. doi: 10.1109/LSP.2018.2792050
23	LI C Y , GUO C L , REN W Q , et al. An underwater image enhancement benchmark dataset and beyond. IEEE Transactions on Image Processing, 2019, 29, 4376- 4389.
24	JAMADANDI A, MUDENAGUDI U. Exemplar-based underwater image enhancement augmented by wavelet corrected Transforms[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Washington D.C., USA: IEEE Press, 2019: 11-17.
25	LI C Y , ANWAR S , HOU J H , et al. Underwater image enhancement via medium transmission-guided multi-color space embedding. IEEE Transactions on Image Processing, 2021, 30, 4985- 5000. doi: 10.1109/TIP.2021.3076367
26	WANG H , SUN S X , BAI X , et al. A reinforcement learning paradigm of configuring visual enhancement for object detection in underwater scenes. IEEE Journal of Oceanic Engineering, 2023, 48 (2): 443- 461. doi: 10.1109/JOE.2022.3226202
27	SUN S X , WANG H , ZHANG H , et al. Underwater image enhancement with reinforcement learning. IEEE Journal of Oceanic Engineering, 2024, 49 (1): 249- 261. doi: 10.1109/JOE.2022.3152519
28	TIAN C W , ZHENG M H , ZUO W M , et al. A cross Transformer for image denoising. Information Fusion, 2024, 102, 102043. doi: 10.1016/j.inffus.2023.102043
29	YAN B, PENG H W, FU J L, et al. Learning spatio-temporal Transformer for visual tracking[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Washington D.C., USA: IEEE Press, 2021: 10428-10437.
30	WANG N, ZHOU W G, WANG J, et al. Transformer meets tracker: exploiting temporal context for robust visual tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2021: 1571-1580.
31	SONG Z K, YU J Q, CHEN Y P, et al. Transformer tracking with cyclic shifting window attention[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2022: 8781-8790.
32	CUI Y T, JIANG C, WANG L M, et al. MixFormer: end-to-end tracking with iterative mixed attention[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2022: 13598-13608.
33	ZHAO H J, WANG D, LU H C. Representation learning for visual object tracking by masked appearance transfer[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2023: 18696-18705.
34	CHEN X, YAN B, ZHU J W, et al. Transformer tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2021: 8122-8131.
35	ZHENG Z H, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2020: 12993-13000.
36	LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[EB/OL]. [2024-03-11]. http://arxiv.org/abs/1405.0312.
37	FU Z H, LIU Q J, FU Z H, et al. STMTrack: template-free visual tracking with space-time memory networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2021: 13769-13778.
38	CHEN B Y, LI P X, BAI L, et al. Backbone is all your need: a simplified architecture for visual object tracking[C]//Proceedings of the European Conference on Computer Vision. Berlin, Germany: Springer, 2022: 375-392.
39	ZHANG Z P, LIU Y H, WANG X, et al. Learn to match: automatic matching network design for visual tracking[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Washington D.C., USA: IEEE Press, 2021: 13319-13328.
40	BHAT G, LAWIN F J, DANELLJAN M, et al. Learning what to learn for video object segmentation[C]//Proceedings of the European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 777-794.
41	TANG F, LING Q. Ranking-based Siamese visual tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2022: 8731-8740.

[1]	ZHOU Xueyang, FU Qiming, CHEN Jianping, CHEN Yanming, LU You, WANG Yunzhe. Document-Level Relation Extraction Method Based on Evidence and Graph Inference: A Case Study of Medical Relationships [J]. Computer Engineering, 2025, 51(1): 106-117.
[2]	XIAO Chaoen, LI Zifan, ZHANG Lei, WANG Jianxin, QIAN Siyuan. Differential Cryptanalysis Based on Transformer Model and Attention Mechanism [J]. Computer Engineering, 2025, 51(1): 156-163.
[3]	HU Yongtao, HUANG Hongqiong. Multi-Branch Clothes-Changing Person Re-Identification with Feature Fusion and Channel Attention [J]. Computer Engineering, 2025, 51(1): 225-234.
[4]	HUO Jiuyuan, SU Hongrui, WU Zeyu, WANG Tingjuan. Road Traffic Small Target Vehicle Detection Algorithm Based on Improved YOLOv8 [J]. Computer Engineering, 2025, 51(1): 246-257.
[5]	ZHENG Yazhou, LIU Wanping, HUANG Dong. A BERT-CNN-GRU Detection Method Based on Attention Mechanism [J]. Computer Engineering, 2025, 51(1): 258-268.
[6]	WANG Qian, ZHANG Junhua, WANG Zetong, LI Bo. X2S-Net: Three-Dimensional Reconstruction of Spine Based on Biplanar X-Rays [J]. Computer Engineering, 2025, 51(1): 277-286.
[7]	LIU Zhong, TANG Hong, WANG Ningzhe, ZHU Chuanrun. Text Summarization Method Incorporating RNN and Sparse Self-Attention [J]. Computer Engineering, 2025, 51(1): 312-320.
[8]	LIU Zhaowei, FANG Yanhong, ZHENG Mingyu, SUO Bin. Lung Disease Diagnosis Method Based on Attention Mechanism and Multi-tasking [J]. Computer Engineering, 2025, 51(1): 332-342.
[9]	ZHANG Tianpeng, HAN Jing, LÜ Xueqiang. Super-Resolution-Aided Small-Target Detection Based on Multi-Task Learning [J]. Computer Engineering, 2024, 50(9): 304-312.
[10]	GUO Min, ZHANG Xihan, LI Yang. Integrated Attentional Teacher Mutual Consistency Semi-Supervised Medical Image Segmentation [J]. Computer Engineering, 2024, 50(9): 313-323.
[11]	ZENG Yuqi, LIU Bo, ZHONG Baichang, ZHONG Jin. Student Classroom Behavior Detection Algorithm Based on Improved YOLOv8 in Smart Education [J]. Computer Engineering, 2024, 50(9): 344-355.
[12]	LI Junjun, DONG Jiangang, LI Kun. Research on Kubernetes-based Cluster Energy-Saving Strategy [J]. Computer Engineering, 2024, 50(9): 82-91.
[13]	LIN Chang, GUO Wei, REN Zhecong, JIN Haibo. Unification Algorithm for Object Tracking and Segmentation Based on Transformer [J]. Computer Engineering, 2024, 50(9): 130-141.
[14]	LI Zelin, LÜ Zhaofeng, CHEN Fuqiang, LI Ke. Entity Alignment Model Based on Multi-Hop Information Fusion [J]. Computer Engineering, 2024, 50(9): 142-152.
[15]	WANG Ruying, MA Jiajun, DONG Jianqiang, LIU Wanlong, ZHANG Haitao, YIN Kai, ZHAO Bochao. Industrial Load Forecasting Method Based on MTS-BiGRU-DMHSA [J]. Computer Engineering, 2024, 50(9): 169-178.

Please choose a citation manager

Content to export