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Deformable Convolutional Networks Tracker
2019
DEStech Transactions on Computer Science and Engineering
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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]
2017
arXiv
pre-print
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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
2017
DEStech Transactions on Computer Science and Engineering
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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]
2020
arXiv
pre-print
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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)
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., +, 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]
2018
arXiv
pre-print
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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
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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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
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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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
2014
Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14
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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
2018
Sensors
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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
2018
Advances in Multimedia
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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
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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) 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
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., +, 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
2022
Applied Sciences
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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]
2014
Lecture Notes in Computer Science
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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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