Channel Positive and Negative Feedback Network for Target Tracking

Aiming at alleviate the detrimental effect of similar object interferences and target state changes in SiamRPN tracker, a Channel Positive and Negative Feedback Network (CPFN) is proposed, in which the Gaussian score map is generated by the feature channels selected by a Gaussian kernel, and the map is combined with the classification branches of SiamRPN. In this way, the feature channels are divided into positive feedback channels and interference channels, and these feature channels are effectively utilized. In addition, a channel weight update strategy is proposed to enhance the robustness of the tracker and avoid template pollution caused by inadequate template update. Extensive experiments on tracking benchmarks including VOT2016, VOT2018, VOT2019, OTB100, UAV123, LaSOT and GOT-10k show that the proposed CPFN outperforms the state-of-the-art methods based on small backbone network in terms of accuracy and achieves high-speed tracking.

[1]  Xiaogang Wang,et al.  Visual Tracking with Fully Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Haibin Ling,et al.  Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Rynson W. H. Lau,et al.  VITAL: VIsual Tracking via Adversarial Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Chong Luo,et al.  A Twofold Siamese Network for Real-Time Object Tracking , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[5]  Stan Sclaroff,et al.  MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization , 2014, ECCV.

[6]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Luca Bertinetto,et al.  Staple: Complementary Learners for Real-Time Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Xiao Chen,et al.  A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning , 2019, Frontiers of Computer Science.

[9]  Michael Felsberg,et al.  ECO: Efficient Convolution Operators for Tracking , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Wei Wu,et al.  Distractor-aware Siamese Networks for Visual Object Tracking , 2018, ECCV.

[11]  Josef Kittler,et al.  Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[12]  Michael Felsberg,et al.  Convolutional Features for Correlation Filter Based Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[13]  Liangpei Zhang,et al.  Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Ahmad Ayatollahi,et al.  Online Visual Tracking with One-Shot Context-Aware Domain Adaptation , 2020, ArXiv.

[15]  Huchuan Lu,et al.  Visual Tracking via Adaptive Spatially-Regularized Correlation Filters , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Bohyung Han,et al.  Learning Multi-domain Convolutional Neural Networks for Visual Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Zhu Lei,et al.  Locality Preserving Joint Transfer for Domain Adaptation , 2019, IEEE Transactions on Image Processing.

[18]  Zhenyu He,et al.  Target-Aware Deep Tracking , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Michael Felsberg,et al.  Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking , 2016, ECCV.

[20]  Simon Lucey,et al.  Learning Background-Aware Correlation Filters for Visual Tracking , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[21]  Michael Felsberg,et al.  Discriminative Scale Space Tracking , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Luca Bertinetto,et al.  End-to-End Representation Learning for Correlation Filter Based Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Zhenyu He,et al.  The Seventh Visual Object Tracking VOT2019 Challenge Results , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[24]  Michael Felsberg,et al.  Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Xiaoping Liu,et al.  Domain Adaption for Fine-Grained Urban Village Extraction From Satellite Images , 2020, IEEE Geoscience and Remote Sensing Letters.

[26]  Bruce A. Draper,et al.  Visual object tracking using adaptive correlation filters , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Huchuan Lu,et al.  GradNet: Gradient-Guided Network for Visual Object Tracking , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[28]  Xinggang Wang,et al.  Multi-scale multi-patch person re-identification with exclusivity regularized softmax , 2020, Neurocomputing.

[29]  Feng Li,et al.  Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[30]  Kang Li,et al.  Robust Visual Tracking Based on Convolutional Features with Illumination and Occlusion Handing , 2018, Journal of Computer Science and Technology.

[31]  Luca Bertinetto,et al.  Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.

[32]  Huchuan Lu,et al.  Structured Siamese Network for Real-Time Visual Tracking , 2018, ECCV.

[33]  Ming-Hsuan Yang,et al.  Hierarchical Convolutional Features for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[34]  Abhinav Gupta,et al.  Transferring Rich Feature Hierarchies for Robust Visual Tracking , 2015, ArXiv.

[35]  Lu Ke,et al.  Maximum Density Divergence for Domain Adaptation , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Zhiwei Xiong,et al.  SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Qingming Huang,et al.  Hedged Deep Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Jianke Zhu,et al.  A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration , 2014, ECCV Workshops.

[39]  Jianbing Shen,et al.  Local Semantic Siamese Networks for Fast Tracking , 2019, IEEE Transactions on Image Processing.

[40]  Seunghoon Hong,et al.  Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network , 2015, ICML.

[41]  Wei Wu,et al.  High Performance Visual Tracking with Siamese Region Proposal Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[42]  Zhipeng Zhang,et al.  Deeper and Wider Siamese Networks for Real-Time Visual Tracking , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).