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| Target Tracking Method Based on Cross-channel Attention |
| ZHANG Li-guo,GENG Xing-shuo,JIN Mei,ZHANG Yu-peng,ZHANG Sheng |
| School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract Aiming at the problem that the tracking target is blocked or there are sensitive interferences around the target, which leads to the classification error of foreground background and the prediction error of boundary box, an anchor frame-free target tracking method based on cross-channel attention is proposed. Firstly, cross-channel attention is used to enhance the last three layers of output of feature extraction, and the correlation of all channel features is integrated by using the similarity of target in template features and search features, so as to selectively enhance the channel of target features. After that, feature fusion is carried out by weighted summation, and shallow feature fusion and deep feature fusion are used to improve classification accuracy and location accuracy. Finally, location attention is used to enhance the features of the classification feature map again to improve the accuracy of target location. Experimental results show that the proposed algorithm achieves 85.5% precision rate and 64.1% success rate on OTB100 dataset, and 70.5% precision rate and 56.0% success rate on UAV20L dataset.
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Received: 27 April 2022
Published: 18 April 2023
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|
|
|
|
|
| ［5］ |
Gao J, Zhang T, Xu C. Graph convolutional tracking ［C］//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 4649-4659.
|
| ［23］ |
张立国, 杨曼, 周思恩, 等. 基于自适应特征融合的多尺度相关滤波跟踪［J］. 计量学报, 2022, 43(10): 1271-1278.
|
| ［1］ |
孔德明, 杨丹, 王书涛. 交互式多模型集自适应协同滤波目标跟踪算法研究 ［J］. 计量学报, 2021, 42(5): 638-644.
|
|
Kong D M, Yang D, Wang S T. Research on interactive Multi-model set adaptive collaborative filtering target tracking Algorithm ［J］. Acta Metrologica Sinica, 2021, 42(5): 638-644.
|
| ［3］ |
Possegger H, Mauthner T, Bischof H. In defense of color-based model-free tracking ［C］//IEEE conference on computer vision and pattern recognition. 2015: 2113-2120.
|
| ［13］ |
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition ［C］//IEEE conference on computer vision and pattern recognition. 2016: 770-778.
|
| ［16］ |
陈卫东, 陈磊, 邓志巍, 等. 基于混合相关滤波信息融合再检测的目标跟踪算法 ［J］. 计量学报, 2019, 40(6): 1006-1012.
|
|
Chen Z W, Zhang Z X, Song J, et al. Tracking Algorithm for Siamese Network Based on Target-Aware Feature Selection ［J］. Acta Optica Sinica, 40(9): 110-126.
|
| ［19］ |
Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network ［C］// IEEE conference on computer vision and pattern recognition. 2017: 2881-2890.
|
| ［4］ |
Valmadre J, Bertinetto L, Henriques J, et al. End-to-end representation learning for correlation filter based tracking ［C］//IEEE conference on computer vision and pattern recognition. 2017: 2805-2813.
|
| ［6］ |
Zhang L, Varadarajan J, Nagaratnam Suganthan P, et al. Robust visual tracking using oblique random forests ［C］//IEEE conference on computer vision and pattern recognition. 2017: 5589-5598.
|
| ［9］ |
Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks ［J］. Advances in neural information processing systems, 2015, 28: 1137-1149.
|
| ［10］ |
Li B, Yan J, Wu W, et al. High performance visual tracking with siamese region proposal network ［C］// IEEE conference on computer vision and pattern recognition. 2018: 8971-8980.
|
| ［11］ |
Li B, Wu W, Wang Q, et al. Siamrpn++: Evolution of siamese visual tracking with very deep networks ［C］//IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 4282-4291.
|
| ［14］ |
Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks ［J］. Advances in neural information processing systems, 2012, 25: 89-90.
|
| ［18］ |
Zhang Z, Lan C, Zeng W, et al. Relation-aware global attention for person re-identification ［C］// IEEE/CVF conference on computer vision and pattern recognition. 2020: 3186-3195.
|
| ［21］ |
Huang L, Zhao X, Huang K. Got-10k: A large high-diversity benchmark for generic object tracking in the wild ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 43(5): 1562-1577.
|
| ［22］ |
Muller M, Bibi A, Giancola S, et al. Trackingnet: A large-scale dataset and benchmark for object tracking in the wild ［C］//European conference on computer vision. 2018: 300-317.
|
|
Zhang L G, Yang M, Zhou S E, et al. Multi-scale Correlation Filtering Tracking Based on Adaptive Feature Fusion ［J］. Acta Metrologica Sinica, 2022, 43(10): 1271-1278.
|
| ［2］ |
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, 2014, 37(3): 583-596.
|
| ［7］ |
Danelljan M, Bhat G, Khan F S, et al. Accurate tracking by overlap maximization ［C］//IEEE/CVF conference on computer vision and pattern recognition. 2019: 4660-4669.
|
| ［8］ |
Bertinetto L, Valmadre J, Henriques J F, et al. Fully-convolutional siamese networks for object tracking ［C］//European conference on computer vision. Springer, Cham, 2016: 850-865.
|
| ［12］ |
Zhang Z, Peng H. Deeper and wider siamese networks for real-time visual tracking ［C］//IEEE/CVF conference on computer vision and pattern recognition. 2019: 4591-4600.
|
|
Chen W D, Chen L, Deng Z W, et al. Object tracking algorithm based on information fusion and redetection of hybrid correlation filtering ［J］. Acta Metrologica Sinica, 2019, 40(6): 1006-1012.
|
| ［17］ |
陈志旺, 张忠新, 宋娟, 等. 基于目标感知特征筛选的孪生网络跟踪算法 ［J］. 光学学报, 2020, 40(9): 110-126.
|
| ［20］ |
Fu J, Liu J, Tian H, et al. Dual attention network for scene segmentation ［C］// IEEE/CVF conference on computer vision and pattern recognition. 2019: 3146-3154.
|
| ［24］ |
Mueller M, Smith N, Ghanem B. A benchmark and simulator for uav tracking ［C］//European conference on computer vision. Springer, Cham, 2016: 445-461.
|
| ［25］ |
Guo D, Wang J, Cui Y, et al. SiamCAR: Siamese fully convolutional classification and regression for visual tracking ［C］//IEEE/CVF conference on computer vision and pattern recognition. 2020: 6269-6277.
|
| ［15］ |
Xu Y, Wang Z, Li Z, et al. Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines ［C］//AAAI conference on Artificial Intelligence. 2020, 34(7): 12549-12556.
|
|
|
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2023, Vol. 44 
