Attentional Correlation Filter Network for Adaptive Visual Tracking

By Jongwon Choi, Hyung Jin Chang, Sangdoo Yun, Tobias Fischer, Yiannis Demiris, J. Choi. Computer Vision and Pattern Recognition, 2017

304 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/f2c050fa106b2e6f27d32e21e75ecbdb0cc75f67

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Chao Ma
2. Antoni B. Chan
3. Tianyu Yang
4. Qiang Wang
5. Junliang Xing
6. Sangdoo Yun
7. Weiming Hu
8. Yibing Song

In Computer Vision and Pattern Recognition

1. Ming-Hsuan Yang
2. Luc Van Gool
3. Dacheng Tao
4. Xiaogang Wang
5. Martin Danelljan
6. Fahad Shahbaz Khan
7. Yi Yang
8. Philip H. S. Torr

In Safety, Risk, Reliability and Quality

1. Houqiang Li
2. Wen-gang Zhou
3. Ning Wang
4. Qiang Wang
5. Chao Ma
6. Hossein Ghanei-Yakhdan
7. Seyed Mojtaba Marvasti-Zadeh
8. Shohreh Kasaei

Citations in the corpus

• Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking (2018, methodology): “ACFN obtains an AUC score of 60.7%.”
• Target-Aware Deep Tracking (2019, background): “On the other hand, numerous trackers [16, 6, 28, 17, 35, 47, 12] are developed by improving the decision models including support vector machines, correlation filters, deep classifiers, and deep regressors.”
• Deep Learning for Visual Tracking: A Comprehensive Survey (2019, methodology): “20GHz CPU, Titan Xp Pascal GPU Matlab MatConvNet 83 CM ACFN [93] Attentional correlation filter network OTB2013, OTB2015, VOT2014, VOT2015 HOG, Color, DAF Intel I7-6900K 3.”
• Learning Dynamic Memory Networks for Object Tracking (2018, methodology): “We compare our method MemTrack with 9 recent real-time trackers (≥ 15 fps), including CFNet [30], LMCF [32], ACFN [5], RFL [36], SiamFC [4], SiamFC U [30], Staple [3], DSST [7], and KCF [15] on both OTB-2013 and OTB-2015.”
• Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (2017, background): “[5, 6] proposed the integrated tracking system to handle the various type of correlation filters with attentional mechanism.”
• Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking (2019, background): “A neural network is trained based on that score to choose a suitable tracker in the next frame [4].”
• A Twofold Siamese Network for Real-Time Object Tracking (2018, background): “In SCT [4] and ACFN [5], the authors build an attention network to select the best module among several feature extractors for the tracked target.”
• Unsupervised Deep Tracking (2019, methodology): “Qualitative evaluation of our proposed UDT and other trackers including SiamFC [1], CFNet [49], ACFN [6], and DSST [8] on 8 challenging videos from OTB-2015.”
• Attention, please! A survey of neural attention models in deep learning (2021, methodology): “This approach was fundamental to inspire current methods that use attention as a switch between layers (Choi et al. 2017).”