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Towards High Performance Video Object Detection for Mobiles [article]

Xizhou Zhu, Jifeng Dai, Xingchi Zhu, Yichen Wei, Lu Yuan
2018 arXiv   pre-print
Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles.  ...  The proposed system achieves 60.2% mAP score at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8).  ...  On all frames, we present Light Flow, a very small deep neural network to estimate feature flow, which offers instant availability on mobiles.  ... 
arXiv:1804.05830v1 fatcat:xhsyw532aba3rds34nuhwzk4iy

Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device

Zhibin Cheng, Fuquan Zhang
2020 Wireless Communications and Mobile Computing  
Firstly, a new end-to-end flower detection anchor-based method is inserted into the architecture of the network to make it more precious and fast and the loss function and attention mechanism are introduced  ...  In this paper, a novel flower detection application anchor-based method is proposed, which is combined with an attention mechanism to detect the flowers in a smart garden in AIoT more accurately and fast  ...  Acknowledgments Our work is supported by the National Natural Science Foundation of China, Project No.: 61503082, Project name: Research on Large-Scale Ontology Matching Based on Evolutionary Algorithm  ... 
doi:10.1155/2020/8870649 doaj:97971c33793e4b9cb785ad464a4e26fa fatcat:t3jpsc4drvds7ekcutsyyq2k7q

Visual Feature Learning on Video Object and Human Action Detection: A Systematic Review

Dengshan Li, Rujing Wang, Peng Chen, Chengjun Xie, Qiong Zhou, Xiufang Jia
2021 Micromachines  
detection and using-temporal-information detection; the methods of utilizing temporal information of adjacent video frames are mainly the optical flow method, Long Short-Term Memory and convolution among  ...  Nowadays, the video detection technology is able to implement real-time detection, or high-accurate detection of blurry video frames.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/mi13010072 pmid:35056238 pmcid:PMC8781209 fatcat:kdc5msiv2rd7zh7qlxymbpdk3y

Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey

Ilias Papadeas, Lazaros Tsochatzidis, Angelos Amanatiadis, Ioannis Pratikakis
2021 Applied Sciences  
Semantic image segmentation for autonomous driving is a challenging task due to its requirement for both effectiveness and efficiency.  ...  Finally, a fruitful discussion is presented that provides key insights for the current trend and future research directions in real-time semantic image segmentation with deep learning for autonomous driving  ...  We would also like to thank NVIDIA Corporation, which kindly donated the Titan X GPU, that has been used for this research. Conflicts of Interest: The authors declare no conflict of interest. Appl.  ... 
doi:10.3390/app11198802 fatcat:2smp4jcvy5fqvkysf74mxgnmce

Stacked Hourglass Network for Robust Facial Landmark Localisation

Jing Yang, Qingshan Liu, Kaihua Zhang
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Then we employ a deep convolutional neural network named Stacked Hourglass Network to increase the capacity of the regression model.  ...  Extensive experiments on four challenging datasets, COFW, IBUG, 300W and the Menpo Benchmark, prove the effectiveness of the proposed method.  ...  More recently, to address the aforementioned limitations of prior work, many algorithms based on deep convolutional neural network have been proposed which benefits from the strong discriminative deep  ... 
doi:10.1109/cvprw.2017.253 dblp:conf/cvpr/YangLZ17 fatcat:qytfjbz7knaajpeyvgpr67t2km

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Shibo Zhang, Yaxuan Li, Shen Zhang, Farzad Shahabi, Stephen Xia, Yu Deng, Nabil Alshurafa
2022 Sensors  
Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices.  ...  trends, and major challenges.  ...  Acknowledgments: Special thanks to Haik Kalamtarian and Krystina Neuman for their valuable feedback. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22041476 pmid:35214377 pmcid:PMC8879042 fatcat:vp6jssypezbd5cnyzn4g35eqrm

Image Segmentation Using Deep Learning: A Survey [article]

Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, Demetri Terzopoulos
2020 arXiv   pre-print
convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings  ...  We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this  ...  ACKNOWLEDGMENTS The authors would like to thank Tsung-Yi Lin from Google Brain, and Jingdong Wang and Yuhui Yuan from Microsoft Research Asia, for reviewing this work, and providing very helpful comments  ... 
arXiv:2001.05566v5 fatcat:wiep26nijncwxjojxbzrqoonti

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances [article]

Shibo Zhang, Yaxuan Li, Shen Zhang, Farzad Shahabi, Stephen Xia, Yu Deng, Nabil Alshurafa
2022 arXiv   pre-print
Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices.  ...  trends, and major challenges.  ...  Acknowledgments Special thanks to Haik Kalamtarian and Krystina Neuman for their valuable feedback.  ... 
arXiv:2111.00418v5 fatcat:wylhzwkndjar7fc3esvhca2axi

A Review of Video Object Detection: Datasets, Metrics and Methods

Haidi Zhu, Haoran Wei, Baoqing Li, Xiaobing Yuan, Nasser Kehtarnavaz
2020 Applied Sciences  
An overview of the existing datasets for video object detection together with commonly used evaluation metrics is first presented.  ...  Video object detection methods are then categorized and a description of each of them is stated.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app10217834 fatcat:qyp2b5guovftplzmmmnec33bdm

Real-Time Facial Segmentation and Performance Capture from RGB Input [article]

Shunsuke Saito, Tianye Li, Hao Li
2016 arXiv   pre-print
Along with recent breakthroughs in deep learning, we demonstrate that pixel-level facial segmentation is possible in real-time by repurposing convolutional neural networks designed originally for general  ...  We adopt a state-of-the-art regression-based facial tracking framework with segmented face images as training, and demonstrate accurate and uninterrupted facial performance capture in the presence of extreme  ...  Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.  ... 
arXiv:1604.02647v1 fatcat:i3bdzgot4ffixn4jcm7hqyb7ze

Low-Power Computer Vision: Status, Challenges, Opportunities [article]

Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko (+28 others)
2019 arXiv   pre-print
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile  ...  Meanwhile, mobile phones have become the primary computing platforms for millions of people.  ...  First, a neural network model needs to be built and trained to identify and classify images Then, the model should run as accurate and fast as possible.  ... 
arXiv:1904.07714v1 fatcat:nfss4oa5mfbr7gb34ky2q6fmsq

A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network

Li, Wang, Xie, Liu, Zhang, Li, Wang, Zhou, Liu
2020 Sensors  
To construct video detection system for plant diseases and pests, and to build a real-time crop diseases and pests video detection system in the future, a deep learning-based video detection architecture  ...  with a custom backbone was proposed for detecting plant diseases and pests in videos.  ...  With the proposal of convolutional neural networks (CNN) [8] , region-based convolutional neural network (R-CNN) [9] , Fast-RCNN [10] and Faster-RCNN [11] , image object detection has been developed  ... 
doi:10.3390/s20030578 pmid:31973039 pmcid:PMC7038217 fatcat:6zqqyjkeezcrbjst6ht5kiqn5u

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

Normaisharah Mamat, Mohd Fauzi Othman, Rawad Abdoulghafor, Samir Brahim Belhaouari, Normahira Mamat, Shamsul Faisal Mohd Hussein
2022 Agriculture  
Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts.  ...  For complicated and ambiguous situations, deep learning technology provides accurate predictions.  ...  The authors would like to acknowledge the Qatar National Library for supporting the publication of this article and Universiti Teknologi Malaysia (Professional Development Research University research  ... 
doi:10.3390/agriculture12071033 fatcat:kdbt3pqdz5hurmxt3jss3ez7ne

Real-Time Facial Segmentation and Performance Capture from RGB Input [chapter]

Shunsuke Saito, Tianye Li, Hao Li
2016 Lecture Notes in Computer Science  
Along with recent breakthroughs in deep learning, we demonstrate that pixel-level facial segmentation is possible in real-time by repurposing convolutional neural networks designed originally for general  ...  We adopt a state-of-the-art regressionbased facial tracking framework with segmented face images as training, and demonstrate accurate and uninterrupted facial performance capture in the presence of extreme  ...  We also thank Rui Saito and Frances Chen for being our capture models.  ... 
doi:10.1007/978-3-319-46484-8_15 fatcat:g7qrehekynfdxpg5v3u3qdnxoe

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance [article]

Aref Miri Rekavandi, Lian Xu, Farid Boussaid, Abd-Krim Seghouane, Stephen Hoefs, Mohammed Bennamoun
2022 arXiv   pre-print
Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects.  ...  In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the  ...  ACKNOWLEDGEMENT This research is supported by the Commonwealth of Australia as represented by the Defence Science and Technology Group of the Department of Defence.  ... 
arXiv:2207.12926v1 fatcat:fjcuijt2f5d63apgg67eiydofa
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