Proceedings Article10.1109/CVPR.2019.00478
Graph Convolutional Tracking
Junyu Gao,Tianzhu Zhang,Changsheng Xu +2 more
- 01 Jun 2019
- pp 4649-4659
TL;DR: The GCT jointly incorporates two types of Graph Convolutional Networks into a siamese framework for target appearance modeling and adopts a spatial-temporal GCN to model the structured representation of historical target exemplars.
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Abstract: Tracking by siamese networks has achieved favorable performance in recent years. However, most of existing siamese methods do not take full advantage of spatial-temporal target appearance modeling under different contextual situations. In fact, the spatial-temporal information can provide diverse features to enhance the target representation, and the context information is important for online adaption of target localization. To comprehensively leverage the spatial-temporal structure of historical target exemplars and get benefit from the context information, in this work, we present a novel Graph Convolutional Tracking (GCT) method for high-performance visual tracking. Specifically, the GCT jointly incorporates two types of Graph Convolutional Networks (GCNs) into a siamese framework for target appearance modeling. Here, we adopt a spatial-temporal GCN to model the structured representation of historical target exemplars. Furthermore, a context GCN is designed to utilize the context of the current frame to learn adaptive features for target localization. Extensive results on 4 challenging benchmarks show that our GCT method performs favorably against state-of-the-art trackers while running around 50 frames per second.
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Citations
SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking
Dongyan Guo,Jun Wang,Ying Cui,Zhenhua Wang,Shengyong Chen +4 more
- 14 Jun 2020
TL;DR: A novel fully convolutional Siamese network to solve visual tracking end-to-end in a per-pixel manner by decomposing the visual tracking task into two subproblems as classification for pixel category and regression for object bounding box at this pixel is proposed.
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking
Ning Wang,Wengang Zhou,Jie Wang,Houqiang Li +3 more
- 20 Jun 2021
TL;DR: In this article, a Siamese-like tracking pipeline is proposed to exploit the rich temporal contexts among successive frames, which have been largely overlooked in existing trackers. And the proposed transformer-assisted tracking framework is neat and trained in an end-to-end manner.
Siam R-CNN: Visual Tracking by Re-Detection
Paul Voigtlaender,Jonathon Luiten,Philip H. S. Torr,Bastian Leibe +3 more
- 14 Jun 2020
TL;DR: This work presents Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking, and combines this with a novel tracklet-based dynamic programming algorithm to model the full history of both the object to be tracked and potential distractor objects.
Ocean: Object-aware Anchor-free Tracking
Zhipeng Zhang,Houwen Peng,Jianlong Fu,Bing Li,Weiming Hu +4 more
- 23 Aug 2020
TL;DR: TracKit as discussed by the authors proposes an object-aware anchor-free network to directly predict the position and scale of target objects in an anchor free fashion, which can further contribute to the classification of target object and background.
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•Posted Content
Ocean: Object-aware Anchor-free Tracking
TL;DR: This paper introduces a feature alignment module to learn an object-aware feature from predicted bounding boxes that can further contribute to the classification of target objects and background and presents a novel tracking framework based on the anchor-free model.
References
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
- 06 Sep 2014
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
•Posted Content
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
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