Abstract：Designing a robust tracking algorithm based on correlation filters is an important research direction in the target tracking. The information of background, space and time is significant for improving the tracking performance of the algorithm. Grounded on the background-aware tracking algorithm, an object tracking algorithm based on accelerated adaptive spatial-temporal background aware correlation filter is proposed by fusing the spatial information, temporal information and the adaptability of the spatial weight matrix. Then,the appearance optimization model is solved by the accelerated alternating direction method of multipliers to obtain the spatial weight matrix and the correlation filter to realize the adaptive tracking. The proposed tracking algorithm enhances the discrimination of the tracker for the object from the background with the background information, spatial information and the adaptive spatial weight. The problem of tracking drifting for the case of target occlusion is alleviated by the temporal-regularization term, and the solving process is speeded up by the accelerated alternating direction method of multipliers. Experiments illustrate that the proposed algorithm produces better tracking results in the cases of target occlusion and background interference.
[1] 陈灿,陈昭炯,顾杨,等.尺度感知的分块协同式相关滤波跟踪算法.模式识别与人工智能, 2019, 32(10): 869-881. (CHEN C, CHEH Z J, GU Y, et al. Scale-Aware Partition-Based Cooperative Correlation Filter Tracking Algorithm. Pattern Recognition and Artificial Intelligence, 2019, 32(10): 869-881.) [2] 汤张泳,吴小俊,朱学峰.多空间分辨率自适应特征融合的相关滤波目标跟踪算法.模式识别与人工智能,2020, 33(1): 66-74. (TANG Z Y, WU X J, ZHU X F.Object Tracking with Multi-spatial Resolutions and Adaptive Feature Fusion Based on Correlation Filters. Pattern Recognition and Artificial Intelligence, 2020, 33(1): 66-74.) [3] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual Object Tracking Using Adaptive Correlation Filters // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2010: 2544-2550. [4] HENRIQUES J F, CASEIR R, MARTINS P, et al. High-Speed Tracking with Kernelized Correlation Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596. [5] DANELLJAN M, HÄGER G, KHAN F S, et al. Accurate Scale Estimation for Robust Visual Tracking[C/OL]. [2021-04-26]. http://www.bmva.org/bmvc/2014/files/paper038.pdf. [6] DANELLJAN M, HÄGER G, KHAN F S, et al. Learning Spatially Regularized Correlation Filters for Visual Tracking // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 4310-4318. [7] LI F, TIAN C, ZUO W M, et al. Learning Spatial Temporal Regularized Correlation Filters for Visual Tracking // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 4904-4913. [8] GALOOGAHI H K, FAGG A, LUCEY S.Learning Background-Aware Correlation Filters for Visual Tracking // Proc of the IEEE International Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 1144-1152. [9] DAI K N, WANG D, LU H C, et al. Visual Tracking via Adaptive Spatially-Regularized Correlation Filter // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 4665-4674. [10] BOYD S, PARIKH N, CHU E, et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Foundations and Trends in Machine Learning, 2011, 3(1): 1-122. [11] OUYANG Y Y, CHEN Y M, LAN G H, et al. An Accelerated Linearized Alternating Direction Method of Multipliers. SIAM Journal on Imaging Sciences, 2016, 8(1): 644-681. [12] WU Y, LIM J, YAND M H.Online Object Tracking: A Benchmark // Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2013: 2411-2418. [13] WU Y, LIM J, YAND M H.Object Tracking Benchmark // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2013: 2411-2418. [14] LI S Y, YEUNG D Y.Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models // Proc of the 31st AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2017: 4140-4146. [15] KRISTON M, LEONARDIS A, MATAS J, et al. The Visual Object Tracking VOT2016 Challenge Results // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 777-823. [16] LIANG P P, ERIK B, LING H B.Encoding Color Information for Visual Tracking: Algorithms and Benchmark. IEEE Transactions on Image Processing, 2015, 24(12): 5630-5644. [17] LI Y, ZHU J K.A Scale Adaptive Kernel Correlation Filter Trac-ker with Feature Integration // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2014: 254-265. [18] MA C, HUNG J B, YANG X K, et al. Hierarchical Convolutional Features for Visual Tracking // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 3074-3082. [19] YUAN D, SHU X, HE Z Y.TRBACF: Learning Temporal Regularized Correlation Filters for High Performance Online Visual Object Tracking. Journal of Visual Communication and Image Representation, 2020. DOI: 10.1016/j.jvcir.2020.102882. [20] PU L, FENG X X, HOU Z Q.Learning Temporal Regularized Correlation Filter Tracker with Spatial Reliable Constraint. IEEE Access, 2019, 7: 81441-81450.
2022, Vol. 35 
