|
|
|
| Target Tracking Algorithm Based on Normalized Attention Mechanism with Feature Adaptive Fusion |
| ZHANG Li-guo1,2,ZHANG Yu-peng1,2,JIN Mei1,2,ZHANG Sheng1,2,GENG Xing-shuo1,2 |
1. Hebei Key Laboratory of Measurement Technology and Instrument, Yanshan University,
Qinhuangdao, Hebei 066004, China
2.School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
|
|
|
|
Abstract To solve the difficult problems of image deformation and low resolution in fast moving target tracking. A feature-adaptive fusion target tracking algorithm based on a normalised attention mechanism is proposed based on current siamese networks. Firstly, the less obvious weights are suppressed by a lightweight attention mechanism that imposes a weight sparsity penalty on the attention module. While a path enhancement method is proposed, path strengthening is applied to the last four feature layers of the backbone. Secondly, a plug-in template online update method is proposed in order to capture changes in the appearance of the target during online tracking and improve the robustness of the algorithm. Finally, a regression-enhanced classification method is used to complete the tracking of the target. The experimental results show that the proposed algorithm achieves high success rates of 63.3% and 59.5% on two challenging datasets, OTB100,UAV123, respectively. Also, the algorithm has strong robustness when the illumination changes, the image background is complex and the target is rotated in-plane.
|
|
Received: 27 April 2022
Published: 21 September 2023
|
|
|
| Fund:Science and technology Support Program of Science and Technology Research and Development Program of Hebei Province;The central government of Hebei province guides local special projects |
|
|
|
| ［4］ |
Zhang Z, Peng H. Deeper and wider siamese networks for real-time visual tracking［C］//IEEE Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019.
|
| ［18］ |
谭芳, 穆平安, 马忠雪. 基于YOLOv3检测和特征点匹配的多目标跟踪算法［J］. 计量学报, 2021, 42(2): 157-162.
|
| ［1］ |
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.
|
| ［3］ |
Li B, Wu W, Wang Q, et al. Siamrpn++: Evolution of siamese visual tracking with very deep networks［C］//IEEE Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019.
|
| ［5］ |
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. New York, USA, 2020.
|
| ［8］ |
Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module［C］//The European Conference on Computer Vision(ECCV). Munich, Germany, 2018.
|
| ［11］ |
Wu Y, Lim J, Yang M H. Online object tracking: A benchmark［C］//IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013.
|
| ［13］ |
Liu Y, Shao Z, Teng Y, et al. NAM: Normalization-based Attention Module［C］//Conference and Workshop on Neural Information Processing Systems. Online, 2021.
|
| ［15］ |
Chen Z, Zhong B, Li G, et al. Siamese box adaptive network for visual tracking［C］//IEEE Conference on Computer Vision and Pattern Recognition. Seattle, USA, 2020.
|
| ［17］ |
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.
|
| ［22］ |
Deng J, Dong W, Socher R, et al. Imagenet: A large-scale hierarchical image database［C］//IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009.
|
| ［6］ |
Guo D, Wang J, Cui Y, et al. SiamCAR: Siamese fully convolutional classification and regression for visual tracking［C］//IEEE Conference on Computer Vision and Pattern Recognition. Seattle, USA, 2020.
|
| ［9］ |
Li X, Wang W, Hu X, et al. Generalized focal loss v2: Learning reliable localization quality estimation for dense object detection［C］//IEEE Conference on Computer Vision and Pattern Recognition. Montreal, Canada, 2021.
|
| ［16］ |
程淑红, 王迎. 遮挡和光照变化下的单鱼目标跟踪［J］. 计量学报, 2021, 42(2): 171-177.
|
|
Chen W D, Chen L, Deng Z W, et al. Target tracking algorithm based on hybrid correlation filtering and information fusion redetection［J］. Acta Metrolgica Sinica, 2019, 40(6): 1006-1012.
|
| ［7］ |
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.
|
|
Cheng S H, Wang Y. Single fish tracking under occlusion and light change［J］. Acta Metrolgica Sinica, 2021, 42(2): 171-177.
|
|
Tan F, Mu P A, Ma Z X. Multi-target tracking algorithm based on YOLOv3 detection and feature point matching［J］. Acta Metrolgica Sinica, 2021, 42(2): 157-162.
|
| ［20］ |
Fan H, Lin L, Yang F, et al. Lasot: A high-quality benchmark for large-scale single object tracking［C］//IEEE Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019.
|
| ［2］ |
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. Salt Lake city, USA, 2018.
|
| ［10］ |
Danelljan M, Bhat G, Khan F S, et al. Atom: Accurate tracking by overlap maximization［C］//IEEE Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019.
|
| ［12］ |
Russakovsky O, Deng J, Su H, et al. Imagenet large scale visual recognition challenge［J］. International Journal of Computer Vision, 2015, 115(3): 211-252.
|
| ［14］ |
Yu Y, Xiong Y, Huang W, et al. Deformable siamese attention networks for visual object tracking［C］//IEEE Conference on Computer Vision and Pattern Recognition. Seattle, USA, 2020.
|
| ［19］ |
Zhou J, Wang P, Sun H. Discriminative and robust online learning for siamese visual tracking［C］//AAAI Conference on Artificial Intelligence. New York, USA, 2020.
|
| ［21］ |
Lin T Y, Maire M, Belongie S, et al. Microsoft coco: Common objects in context［C］//European Conference on Computer Vision. Springer, Cham, 2014.
|
|
|
|
2023, Vol. 44 
