高斯感知约束空间异常的相关滤波目标跟踪

doi:10.3778/j.issn.1002-8331.2211-0408

摘要/Abstract

摘要： 针对目标在复杂场景运动过程中容易出现跟踪丢失问题，提出一种高斯感知约束空间异常的目标跟踪算法。以高斯均匀分布为分布规律建立目标特征采样点，采用卷积结构提取目标的外观模型以及权重模型；为了约束空间异常，在目标函数中构建空间正则项，同时更新目标权重模型，减小空间过拟合的产生，增强跟踪器的空间异常适应性；应用加权最小二乘法思想，获得权重响应模型中心，确定目标中心，更新跟踪位置，增强跟踪器鲁棒性。使用OTB2015和UAV20L数据集，与其他主流相关滤波算法相比，该算法在目标运动导致低分辨率、遮挡等复杂条件下，跟踪成功率以及跟踪精度较高。

关键词: 机器视觉, 约束空间异常, 空间正则, 相关滤波

Abstract: In an effort to solve the loss of target tracking in complicated movements, a target tracking algorithm with Gaussian-aware constraint space anomaly is proposed. Firstly, the feature sampling points of the target are established with Gaussian uniform distribution as the distribution law, and the appearance model and weight model of the target are extracted with convolution structure. Secondly, in an effort to constrain spatial anomaly, spatial regular terms are constructed in the target function; while at the same time the target weight model is updated to minimize the occurrence of spatial overfitting, thereby enhancing the spatial anomaly adaptability of the tracker. Lastly, the weighted least square method is applied to obtain the weight response model center, so as to determine the target center, update the tracking position, thereby enhancing the robustness of the tracker. By means of OTB2015 and UAV20L dataset, the algorithm proposed in this paper, when compared with other mainstream relevant filtering algorithms, presents high tracking success rate and tracking accuracy under such complicated circumstances as low resolution and obstruction due to target motion.

Key words: machine vision, constraint space exception, space regularity, correlation filtering

姜文涛, 王梓民, 张晟翀. 高斯感知约束空间异常的相关滤波目标跟踪[J]. 计算机工程与应用, 2024, 60(7): 238-247.

JIANG Wentao, WANG Zimin, ZHANG Shengchong. Learning Gaussian-Aware Constraint Spatial Anomaly for Correlated Filter Target Tracking[J]. Computer Engineering and Applications, 2024, 60(7): 238-247.

参考文献

[1] THAVAMANI C, LI M, CEBRON N, et al. Fovea: foveated image magnification for autonomous navigation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 15539-15548.
[2] KERKETTA R, ERASMUS M F, WILSON B S, et al. Spatial stochastic model of the pre-B cell receptor[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023, 20(1): 683-693.
[3] KAVYA J. Internet of drones (IOD) in medical transport application[M/OL]//Healthcare monitoring and data analysis using IoT: technologies and applications, 2022: 345-353.
[4] HAO X, XIA Y, YANG H, et al. Asynchronous information fusion in intelligent driving systems for target tracking using cameras and radars [J]. IEEE Transactions on Industrial Electronics, 2023, 70(3): 2708-2717.
[5] TESFAYE Y T, ZEMENE E, PRATI A, et al. Multi-target tracking in multiple non-overlapping cameras using fast-constrained dominant sets[J]. International Journal of Computer Vision, 2019, 127(9): 1303-1320.
[6] 赵运基, 范存良, 张新良. 融合多特征和通道感知的目标跟踪算法[J]. 计算机科学与探索, 2022, 16(6): 1417-1428.
ZHAO Y J, FAN C L, ZHANG X L. Object tracking algorithm with fusion of multi-feature and channel awareness[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(6): 1417-1428.
[7] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proc of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010: 2544-2550.
[8] HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596.
[9] 刘艺, 李蒙蒙, 郑奇斌, 等. 视频目标跟踪算法综述[J]. 计算机科学与探索, 2022, 16(7): 1504-1515.
LIU Y, LI M M, ZHENG Q B, et al. Survey on video object tracking algorithms[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(7): 1504-1515.
[10] DANELLJAN M, H?GER G, KHAN F, et al. Accurate scale estimation for robust visual tracking[C]//Proceedings of the British Machine Vision Conference, Nottingham, 2014.
[11] DANELLJAN M, HAGER G, SHAHBAZ KHAN F, et al. Learning spatially regularized correlation filters for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 4310-4318.
[12] GALOOGAHI H K, FAGG A, LUCEY S. Learning background-aware correlation filters for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 1135-1143.
[13] LI F, TIAN C, ZUO W, et al. Learning spatial-temporal regularized correlation filters for visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 4904-4913.
[14] LI Y, FU C, DING F, et al. AutoTrack: towards high-performance visual tracking for UAV with automatic spatio-temporal regularization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 11923-11932.
[15] XU T, FENG Z, WU X J, et al. Adaptive channel selection for robust visual object tracking with discriminative correlation filters[J]. International Journal of Computer Vision, 2021, 129(5): 1359-1375.
[16] FAN E. Extended tanh-function method and its applications to nonlinear equations[J]. Physics Letters A, 2000, 277(4/5): 212-218.
[17] 张伟俊, 钟胜, 徐文辉, 等. 融合显著性与运动信息的相关滤波跟踪算法[J]. 自动化学报, 2021, 47(7): 17-26.
ZHANG?W J, ZHONG?S, XU?W H. et al. Correlation filter based visual tracking integrating saliency and motion cues[J]. Acta?Automatica?Sinica, 2021, 47(7): 17-26.
[18] DENG C Y. A generalization of the Sheman-Morrison-Woodbury formula[J]. Applied Mathematics Letters, 2011, 24(9): 1561-1564.
[19] DANELLJAN M, BHAT G, SHAHBAZ KHAN F, et al. ECO: efficient convolution operators for tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6638-6646.
[20] ZHANG F, MA S, ZHANG Y, et al. Perceiving temporal environment for correlation filters in real-time UAV tracking[J]. IEEE Signal Processing Letters, 2021, 29: 6-10.
[21] WU Y, LIM J, YANG M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(9): 1834-1848.
[22] MUELLER M, SMITH N, GHANEM B. A benchmark and simulator for UAV tracking[C]//European Conference on Computer Vision. Cham: Springer, 2016: 445-461.