计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 110-117.doi: 10.11896/jsjkx.200800212
郭文1, 尹童灵1, 张天柱2, 徐常胜3
GUO Wen1, YIN Tong-ling1, ZHANG Tian-zhu2, XU Chang-sheng3
摘要： 建立一个既能充分考虑目标表观表达的判别性、又能在后续的跟踪过程中保持特征的时间一致性的模型,是解决跟踪问题的关键。为了提高跟踪算法的特征表达判别性和解决跟踪过程中的特征时效性退化问题,文中提出了一种时间一致性保持的稀疏深度表达的跟踪方法。首先,利用不同卷积层上的特征有不同的属性来构建多任务的稀疏深度表达学习方法,充分挖掘多源信息的相关性。其次,利用相关帧的残差构建时间一致性约束正则项,以对跟踪过程特征的退化起到补偿作用,提高了跟踪算法特征的时间一致性。大量实验视频的跟踪结果显示,相比当前的主流算法,所提算法在复杂背景、快速运动等情况下具有更好的跟踪效果和稳定性。
中图分类号:
| [1]WU Y,LIM J,YANG M H.Online object tracking:A benchmark[C]//IEEE Conference on Computer Vision and Pattern Recognition.2013:2411-2418. [2]SMEULDERS A W,CHU D M,CUCCHIARA R,et al.Visual tracking:an experimental survey[J].IEEE Transaction on Pattern Analysis Machine Intelligence,2014:1442-1468. [3]BOLME D S,BEVERIDGE J R,DRAPER B A,et al.Visual object tracking using adaptive correlation filters[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Reco-gnition.2010:2544-2550. [4]DANELLJAN M,HAGER G,KHAN F S,et al.Accurate scale estimation for robust visual tracking[C]//Proceedings of British Machine Vision Conference.2014:1-11. [5]DANELLJAN M,KHAN F S,FELSBERG M,et al.Adaptivecolor attributes for real-time visual tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2014:1090-1097. [6]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. [7]DANELLJAN M,HÄGER G,KHAN F S,et al.Learning spatially regularized correlation filters for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:4310-4318. [8]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2014:580-587. [9]DENG J,DONG W,SOCHER R,et al.ImageNet:A large-scale hierarchical image database[C]//Proceedings of IEEE Confe-rence on Computer Vision and Pattern Recognition.2009:248-255. [10]LI P,WANG D,WANG L,et al.Deep visual tracking:Review and experimental comparison[J].Pattern Recognition,2018,76:323-338. [11]HONG S,YOU T,KWAK S,et al.Online tracking by learning discriminative saliency map with convolutional neural network[C]//Proceedings of the 32 nd International Conference on Machine Learning.2015:597-606. [12]WANG L,OUYANG W,WANG X,et al.Visual tracking with fully convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:3119-3127. [13]BERTINETTO L,VALMADRE J,HENRIQUES J F,et al.Fully-convolutional siamese networks for object tracking[C]//Proceedings of the European Conference on Computer Vision Workshops.2016:850-865. [14]CUI Z,XIAO S,FENG J,et al.Recurrently target-attendingtracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1449-1458. [15]ZHANG T,GHANEM B,LIU S,et al.Robust visual tracking via multitask sparse learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2012:2042-2049. [16]HONG Z,MEI X,PROKHOROV D,et al.Tracking via Robust Multi-task Multi-view Joint Sparse Representation[C]//Proceedings of the IEEE International Conference on Computer Vision.2013:649-656. [17]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition [J].arXiv:1409.1556,2014. [18]CHEN X,PAN W,KWOK J,et al.Accelerated gradient method for multi-task sparse learning problem[C]//Proceedings of the IEEE International Conference on Data Mining(ICDM).2009:746-751. [19]MEI X,LING H.Robust visual tracking using L1 minimization[C]//Proceedings of the International Conference on Computer Vision.2010:353-371 [20]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. [21]BAO C,WU Y,LING H,et al.Real time robust L1 trackerusing accelerated proximal gradient approach[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2012:1830-1837. [22]ROSS D A,LIM J,LIN R S,et al.Incremental learning for robust visual tracking[J].International Journal of Computer Vision,2008,77(13):125-141. [23]WU Y,SHEN B,LING H.Online robust image alignment viaiterative convex optimization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2012:1808-1814. [24]SEVILLALAR L,LEARNED-MILLER E.Distribution fieldsfor tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2012:1910-1917. [25]ZHANG K,ZHANG L,YANG M H.Real-time compressivetracking[C]//Proceedings of the European Conference on Computer Vision.2012:864-877. [26]EVERINGHAM M,VAN G L,WILLIAMS C K,The pascal vi-sual object classes(voc) challenge[J].International Journal of Computer Vision,2010,88(2):303-338. |
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