结合目标检测的多尺度相关滤波视觉跟踪算法
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摘要
为满足视觉跟踪算法对跟踪精度与跟踪速度的要求,提出一种结合目标检测的多尺度相关滤波视觉跟踪算法。所提算法基于深度学习的目标检测算法找出图像中目标的位置和尺寸,利用相关滤波算法对所给出的目标特征进行视觉跟踪,并在多个尺度中搜索最优响应;当检测到相关滤波响应值异常时,停止对模型更新;当连续数帧响应值异常时,则在全图范围内搜索目标位置和尺寸。所提算法通过对跟踪状态进行评估和模型更新率自适应调整,解决了传统相关滤波类算法跟踪误差随时间积累的问题,且具有较大的跟踪速度和较高的精度。结果表明:在Matlab平台下,所提算法的平均定位精度为0.593,平均交叠率精度为0.784,帧率为65.3 frame/s。
In order to satisfy the requirements of visual tracking algorithm on tracking accuracy and speed, a multiscale correlation filtering visual tracking algorithm combined with target detection is proposed. The proposed algorithm is first used to find the target location and size in the image by the target detection algorithm based on depth learning. The correlation filtering algorithm is then applied to the visual tracking of the given target features and the multi-scale search of the optimal response. When the correlation filtering response appears abnormal, the model stops updating. When the response value of several frames continues to be abnormal, the search of target location and size is then made in the whole image. By the evaluation of tracking states and the adaptive adjustment of model updating rate, the proposed algorithm solves the problem of tracking error accumulation over time in the traditional correlation filter algorithm, and possesses high tracking speed and high precision. The results show that as for the proposed algorithm on the Matlab platform, the average positioning precision is 0. 593, the average overlap precision is 0.784, and the frame rate is 65.3 frame/s.
In order to satisfy the requirements of visual tracking algorithm on tracking accuracy and speed, a multiscale correlation filtering visual tracking algorithm combined with target detection is proposed. The proposed algorithm is first used to find the target location and size in the image by the target detection algorithm based on depth learning. The correlation filtering algorithm is then applied to the visual tracking of the given target features and the multi-scale search of the optimal response. When the correlation filtering response appears abnormal, the model stops updating. When the response value of several frames continues to be abnormal, the search of target location and size is then made in the whole image. By the evaluation of tracking states and the adaptive adjustment of model updating rate, the proposed algorithm solves the problem of tracking error accumulation over time in the traditional correlation filter algorithm, and possesses high tracking speed and high precision. The results show that as for the proposed algorithm on the Matlab platform, the average positioning precision is 0. 593, the average overlap precision is 0.784, and the frame rate is 65.3 frame/s.
引文
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[2] Liu W, Zhao W J, Li C. Long-term visual tracking based on spatio-temporal context[J]. Acta Optica Sinica, 2016, 36(1):0115001.刘威,赵文杰,李成.时空上下文学习长时目标跟踪[J].光学学报,2016, 36(1):0115001.
[3] Cheng Y Z. Mean shift, mode seeking, andclustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8):790-799.
[4] Bradski G R. Computer vision face tracking for use in a perceptual user inferface[C]. IEEE Workshop on Applications of Computer Vision, 1998:214-219.
[5] Kalal Z, Mikolajczyk K, Matas J. Face-TLD:Tracking-learning-detection applied to face[J].Proceedings of the IEEE, 2010, 119(5):3789-3792.
[6] Henriques J F, Caseiro R, Martins P, et al.Exploiting the circulant structure of tracking-bydetection with kernels[C]. European Conference on Computer Vision, 2012:702-715.
[7] Henriques J F, Caseiro R, Martins P, et al. Highspeed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):583-596.
[8] Bolme D S, Beveridge J R, Draper B A, et al. Visual object tracking using adaptive correlation filters[C].IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010:2544-2550.
[9] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005:886-893.
[10] Danelljan M, Hager G, Khan F S, et al.Discriminative scale space tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8):1561-1575.
[11] Wang M M, Liu Y, Huang Z Y. Large margin object tracking with circulant feature maps[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2017:4800-4808.
[12] Shen Q, Yan X L, Liu L F, et al. Multi-scale correlation filtering tracker based on adaptive feature selection[J]. Acta Optica Sinica, 2017,37(5):0515001.沈秋,严小乐,刘霖枫,等.基于自适应特征选择的多尺度相关滤波跟踪[J].光学学报,2017, 37(5):0515001.
[13] Xu Y L, Wang J B, Li Y, et al. Scale-adaptive tracking based on kernelized correlation filter[J].Application Research of Computers, 2016, 33(11):3513-3516, 3520.徐玉龙,王家宝,李阳,等.基于相关滤波的尺度自适应目标跟踪[J].计算机应用研究,2016, 33(11):3513-3516,3520.
[14] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2014:580-587.
[15] Girshick R. Fast R-CNN[J]. IEEE International Conference on Computer Vision and Pattern Recognition, 2015:1440-1448.
[16] Ren S Q, He K M, Girshick R, et al. Faster RCNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[17] Redmon J, Farhadi A. YOLO9000:better, faster,stronger[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2017:6517-6525.
[18] Liu W, Anguelov D, Erhan D, et al. SSD:single shot multibox detector[C]. European Conference on Computer Vision, 2016:21-37.
[19] Simonyan K, Zisserman A.Very deep convolutional networks for large-scale image recognition[J].Computer Science, 2014:arXiv.
[20] Wu Y, Lim J, Yang M H, Online object tracking:a benchmark[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2013:2411-2418.
[21] Bertinetto L, Valmadre J, Golodetz S, et al. Staple:complementary learners for real-time tracking[C].IEEE Conference on Compnter Vision and Pattern Recognition, 2016:1401-1409.
[22] Li Y, Zhu J K. A scale adaptive kernel correlation filter tracked with feature integration[C]. European Conference on Computer Vision, 2014:254-265.
[23] Hare S, Saffari A, Torr P. Struck:structured output tracking with kernels[C]. IEEE International Conference on Computer Vision, 2011:263-270.
[24] Zhang T Z, Ghanem B, Liu S, et al. Robust visual tracking via multi-task sparse learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2012:2042-2049.
[25] Oron S, Bar-Hillel A, Levi D, et al. Locally orderless tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2012:1940-1947.
[26] Perez P, Hue C, Vermaak J, et al. Color-based probabilistic tracking[C]. European Conference on Computer Vision, 2002:661-675.