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Deformable Convolutional Networks Tracker

Wen-ming CAO, Xue-jun CHEN
2019 DEStech Transactions on Computer Science and Engineering  
Although, convolutional neural networks (CNNs) have achieved significant success for visual recognition tasks. Deformation and scale variation of targets are huge challenges in object tracking.  ...  Object tracking is a fundamental topic in computer vision. It is an interdisciplinary scientific filed involving machine learning, pattern recognition etc, and has a wide applicability.  ...  : offline learning, we train the network for 100K iterations with learning rates 0.0001 for convolutional layers, 0.001 for deformable convolutional networks, and 0.001 for fully connected layers.  ... 
doi:10.12783/dtcse/iteee2019/28747 fatcat:pozf7ytmqzfddactm3ukgu2yjm

Deep Reinforcement Learning for Visual Object Tracking in Videos [article]

Da Zhang, Hamid Maei, Xin Wang, Yuan-Fang Wang
2017 arXiv   pre-print
In this paper we introduce a fully end-to-end approach for visual tracking in videos that learns to predict the bounding box locations of a target object at every frame.  ...  Based on this intuition, we formulate our model as a recurrent convolutional neural network agent that interacts with a video overtime, and our model can be trained with reinforcement learning (RL) algorithms  ...  Not only better training and design of recurrent convolutional network can further boost the efficiency and accuracy for visual tracking, but a broad new way of solving vision problem with artificial neural  ... 
arXiv:1701.08936v2 fatcat:csvjdoftvffrrnrsvtvpkpcq6u

Deep Learning Based Visual Tracking: A Review

Chun-bao LI, Bo YANG, Chun-hu LI
2017 DEStech Transactions on Computer Science and Engineering  
As a powerful features learning method, deep learning provides a new way for the realization of visual tracking with higher accuracy and performance.  ...  This paper presents a comprehensive survey on deep learning based visual tracking algorithms.  ...  Although the tracking algorithm effectively decides the best template for visual tracking, the accuracy of the tracker needs to be enhanced.  ... 
doi:10.12783/dtcse/smce2017/12427 fatcat:r5og2dxtr5brxlpxl3cfbnryhm

CNN 101: Interactive Visual Learning for Convolutional Neural Networks [article]

Zijie J. Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng Chau
2020 arXiv   pre-print
We present our ongoing work, CNN 101, an interactive visualization system for explaining and teaching convolutional neural networks.  ...  However, it is often challenging for learners to take the first steps due to the complexity of deep learning models.  ...  Acknowledgements We thank Anmol Chhabria for helping to collect related interactive visual education tools.  ... 
arXiv:2001.02004v2 fatcat:6a7fer5htzczropnsu7rk6v56y

2019 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 29

2019 IEEE transactions on circuits and systems for video technology (Print)  
., +, TCSVT Oct. 2019 2941-2959 Revisiting Jump-Diffusion Process for Visual Tracking: A Reinforcement Learning Approach.  ...  Kamisli, F., TCSVT Feb. 2019 502-516 Diffusion Revisiting Jump-Diffusion Process for Visual Tracking: A Reinforcement Learning Approach.  ... 
doi:10.1109/tcsvt.2019.2959179 fatcat:2bdmsygnonfjnmnvmb72c63tja

Real-time visual tracking by deep reinforced decision making [article]

Janghoon Choi, Junseok Kwon, Kyoung Mu Lee
2018 arXiv   pre-print
The experiment shows that our tracking algorithm runs in real-time speed of 43 fps and the proposed policy network effectively decides the appropriate template for successful visual tracking.  ...  In this paper, we introduce a novel real-time visual tracking algorithm based on a template selection strategy constructed by deep reinforcement learning methods.  ...  Acknowledgments This work was supported by the Visual Turing Test project (IITP-2017-0-01780) from the Ministry of Science and ICT of Korea.  ... 
arXiv:1702.06291v2 fatcat:67vea7zv55g5jcabdf6lnl4pnq

Exploring Fisher vector and deep networks for action spotting

Zhe Wang, Limin Wang, Wenbin Du, Yu Qiao
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Our approach utilizes Fisher vector and iDT features for action spotting, and improve its performance from two aspects: (i) We take account of interaction labels into the training process; (ii) By visualizing  ...  For this reason, we submit the results obtained by our Fisher vector approach which achieves a Jaccard Index of 0.5385 and ranks the 1 st place in track 2.  ...  Figure 3 . 3 The architecture of Spatial and Temporal Convolutional Neural Network for action/interaction recognition.  ... 
doi:10.1109/cvprw.2015.7301330 dblp:conf/cvpr/WangWD015 fatcat:5c7akoaambajfppowkaz7viaaa

Hand-Object Interaction Detection with Fully Convolutional Networks

Matthias Schroder, Helge Ritter
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We present a real-time method that automatically detects hand-object interactions in RGBD sensor data and tracks the object's rigid pose over time.  ...  Detecting hand-object interactions is a challenging problem with many applications in the human-computer interaction domain.  ...  labels for real-time tracking.  ... 
doi:10.1109/cvprw.2017.163 dblp:conf/cvpr/SchroderR17 fatcat:y7visuueibd2nalb7vwvskjcfe

Evolving deep unsupervised convolutional networks for vision-based reinforcement learning

Jan Koutník, Juergen Schmidhuber, Faustino Gomez
2014 Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14  
Dealing with high-dimensional input spaces, like visual input, is a challenging task for reinforcement learning (RL).  ...  The high-dimensional visual input, which the controller would normally receive, is first transformed into a compact feature vector through a deep, max-pooling convolutional neural network (MPCNN).  ...  Acknowledgments This research was supported by Swiss National Science Foundation grant #138219: "Theory and Practice of Reinforcement Learning 2", and EU FP7 project: "NAnoSCale Engineering for Novel Computation  ... 
doi:10.1145/2576768.2598358 dblp:conf/gecco/KoutnikSG14 fatcat:5ew6mz3mlnfctnpewtcpirxjfe

Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature

Yuankun Li, Tingfa Xu, Honggao Deng, Guokai Shi, Jie Guo
2018 Sensors  
Recently, features learned from deep convolutional neural networks (DCNNs) have been used in a variety of visual tasks.  ...  Although correlation filter (CF)-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved.  ...  [12] designed an efficient convolution operators (ECO) for visual tracking using a factorized convolution operation.  ... 
doi:10.3390/s18020653 pmid:29473840 pmcid:PMC5855939 fatcat:mtn77dvux5dqbiljtpszrsldeq

Advanced Visual Analyses for Smart and Autonomous Vehicles

Zhijun Fang, Jenq-Neng Hwang, Shih-Chia Huang
2018 Advances in Multimedia  
More specifically, the paper entitled "Robust Visual Tracking with Discrimination Dictionary Learning" proposes an effective tracking algorithm based on learned discrimination dictionary.  ...  A er several iterations of reviewing processes, five papers are accepted for this special issue, which covers the advance of visual analysis techniques for visual tracking, scene understanding, lane detection  ... 
doi:10.1155/2018/1762428 fatcat:zfhljk3hdndxbnmej2zzgg56ry

Graph Convolutional Tracking

Junyu Gao, Tianzhu Zhang, Changsheng Xu
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
) method for high-performance visual tracking.  ...  Furthermore, a context GCN is designed to utilize the context of the current frame to learn adaptive features for target localization.  ...  One simple yet effective manner of using deep learning for visual tracking is to di-rectly apply siamese networks as a matching function between target object and candidate patches [60, 2, 26, 63, 23,  ... 
doi:10.1109/cvpr.2019.00478 dblp:conf/cvpr/GaoZX19 fatcat:gbvsjl2szjccnciwhajkynipwe

2020 Index IEEE Transactions on Cognitive and Developmental Systems Vol. 12

2020 IEEE Transactions on Cognitive and Developmental Systems  
., +, TCDS Sept. 2020 658-668 Object tracking Memory Mechanisms for Discriminative Visual Tracking Algorithms With Deep Neural Networks.  ...  ., +, TCDS Sept. 2020 439-450 Memory Mechanisms for Discriminative Visual Tracking Algorithms With Deep Neural Networks.  ... 
doi:10.1109/tcds.2020.3044690 fatcat:yfo6c366aramfdltqegqyqphbq

Faster MDNet for Visual Object Tracking

Qianqian Yu, Keqi Fan, Yiyang Wang, Yuhui Zheng
2022 Applied Sciences  
With the rapid development of deep learning techniques, new breakthroughs have been made in deep learning-based object tracking methods.  ...  Simultaneously, we implement an adaptive, spatial pyramid pooling layer for reducing model complexity and accelerating the tracking speed.  ...  First, we introduce a channel attention module after convolutional layers to implement a strategy for capturing cross-channel interactions using fast one-dimensional convolution.  ... 
doi:10.3390/app12052336 fatcat:vj767tq2hzeydbbxvkzzknbowq

Online Evolution of Deep Convolutional Network for Vision-Based Reinforcement Learning [chapter]

Jan Koutník, Jürgen Schmidhuber, Faustino Gomez
2014 Lecture Notes in Computer Science  
Dealing with high-dimensional input spaces, like visual input, is a challenging task for reinforcement learning (RL).  ...  The Max-Pooling Convolutional Neural Network (MPCNN) compressor is evolved online, maximizing the distances between normalized feature vectors computed from the images collected by the recurrent neural  ...  Acknowledgments This research was supported by Swiss National Science Foundation grant #138219: "Theory and Practice of Reinforcement Learning 2", and EU FP7 project: "NAnoSCaleEngineering for Novel Computation  ... 
doi:10.1007/978-3-319-08864-8_25 fatcat:i7ltnilzkjgndltzx6vc3bipvq
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