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Deep Homography Estimation for Dynamic Scenes [article]

Hoang Le, Feng Liu, Shu Zhang, Aseem Agarwala
2020 arXiv   pre-print
This paper investigates and discusses how to design and train a deep neural network that handles dynamic scenes. We first collect a large video dataset with dynamic content.  ...  We then develop a multi-scale neural network and show that when properly trained using our new dataset, this neural network can already handle dynamic scenes to some extent.  ...  Fig. 1 (bottom) and Fig. 2 are used under a Creative Commons license from Youtube users Nikki Limo, chad schollmeyer, Lumnah Acres, Liziqi, Dielectric Videos, and 3DMachines.  ... 
arXiv:2004.02132v1 fatcat:b3ftjjdvznavjlbivwa4bojp3q

A Survey on Video Classification Methods Based on Deep Learning

2019 DEStech Transactions on Computer Science and Engineering  
This paper summarized the video classification methods based on deep learning and analyzed the differences in performance of typical algorithms, meanwhile, summarized the commonly used video classification  ...  After deep learning method successfully applied in image and audio fields, the study of video classification has gradually shifted to deep learning direction.  ...  Deep learning relies on deep neural network back propagation to autonomously extract the depth features of input video.  ... 
doi:10.12783/dtcse/cisnrc2019/33301 fatcat:fbvt4nxfe5bdleh4mocyoopzzy

Efficient Video Fire Detection Exploiting Motion-Flicker-based Dynamic Features and Deep Static Features (April 2020)

Yakun Xie, Jun Zhu, Yungang Cao, Yunhao Zhang, Dejun Feng, Yuchun Zhang, Min Chen
2020 IEEE Access  
INDEX TERMS Fire detection, motion-flicker-based dynamic features, deep static features, background subtraction, flicker detection, adaptive lightweight convolutional neural network.  ...  Convolutional neural networks (CNNs) have been used for a variety of high-performance computer vision tasks.  ...  In addition to our proposed method, four commonly used deep neural networks were selected for use in place of the network proposed in this paper for comparison.  ... 
doi:10.1109/access.2020.2991338 fatcat:rgzizayxgrgsrku5toawrqmbcy

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 Nov. 2019 3247-3257 Trajectory-Pooled Spatial-Temporal Architecture of Deep Convolutional Neural Networks for Video Event Detection.  ...  Pang, M., +, TCSVT Nov. 2019 3184-3198 Trajectory-Pooled Spatial-Temporal Architecture of Deep Convolutional Neural Networks for Video Event Detection.  ... 
doi:10.1109/tcsvt.2019.2959179 fatcat:2bdmsygnonfjnmnvmb72c63tja

2020 Index IEEE Journal of Selected Topics in Signal Processing Vol. 14

2020 IEEE Journal on Selected Topics in Signal Processing  
., +, JSTSP May 2020 817-827 Acceleration of Deep Convolutional Neural Networks Using Adaptive Filter Pruning.  ...  ., +, JSTSP May 2020 715-726 Convolutional neural networks A Domain Enriched Deep Learning Approach to Classify Atherosclerosis Using Intravascular Ultrasound Imaging.  ... 
doi:10.1109/jstsp.2020.3029672 fatcat:6twwzcqpwzg4ddcu2et75po77u

Salient Object Detection in Video using Deep Non-Local Neural Networks [article]

Mohammad Shokri, Ahad Harati, Kimya Taba
2018 arXiv   pre-print
This paper investigates the use of recently introduced non-local neural networks in video salient object detection.  ...  A novel deep non-local neural network architecture is introduced for video salient object detection and tested on two well-known datasets DAVIS and FBMS.  ...  In recent years, due to the successful deployment of deep neural networks in applications such as object detection [19, 55, 46] and image/video segmentation [7, 65, 56] , these models have been the  ... 
arXiv:1810.07097v1 fatcat:usc6h26hvnfphohkzeamrwopxe

2019 IEEE Ninth International Conference on Intelligent Computing and Information System (ICICIS 2019)

2019 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)  
: Egyptian Roads Case Study 202 Mohamed Hussein DDoS attack detection and classification via Convolutional Neural Network (CNN) 233 Mohamed Hussein Kamal Violence Recognition from Videos using Deep Learning  ...  System using Deep Learning 160 Dina Khattab Violence Recognition from Videos using Deep Learning Techniques 80 Ehab Said Small Objects Detection in Satellite Images Using Deep Learning 86 El-Sayed  ... 
doi:10.1109/icicis46948.2019.9014855 fatcat:5pvzp55n7rhyrkszra52ybkwy4

A survey of deepfakes in terms of deep learning and multimedia forensics

Wildan Jameel Hadi, Suhad Malallah Kadhem, Ayad Rodhan Abbas
2022 International Journal of Power Electronics and Drive Systems (IJPEDS)  
Deepfakes is used to completely modify video (or image) content to display something that was not in it originally.  ...  The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection of deepfakes  ...  CREATION OF DEEPFAKES Deepfakes technology can be used as a synonym for any video or image that has been manipulated with the use of deep neural networks.  ... 
doi:10.11591/ijece.v12i4.pp4408-4414 fatcat:32xobok4pfezhep6cqv6xxpube


Akshara Alex, Ashi Sahu, Avni Tanwar, Nisha Rathi, Kavita Namdev
2020 International Journal of Engineering Applied Sciences and Technology  
In the proposed solution, the concept of basic deep neural network model has been widely adopted.  ...  This paper proposes an abnormal event detection system through surveillance camera using machine vision, in corporation with deep learning, which analyzes footages of crowded scenes and detects abnormal  ...  CNN Convolution Neural Network is a part of deep neural network to analyze and process any image.  ... 
doi:10.33564/ijeast.2020.v04i12.028 fatcat:2rvyjx65rnc2vem4cg5jjcttge

Video Salient Object Detection via Fully Convolutional Networks

Wenguan Wang, Jianbing Shen, Ling Shao
2018 IEEE Transactions on Image Processing  
This paper proposes a deep learning model to efficiently detect salient regions in videos.  ...  The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively.  ...  CONCLUSION In this work, we have presented a deep learning method for fast video saliency detection using convolutional neural networks.  ... 
doi:10.1109/tip.2017.2754941 pmid:28945593 fatcat:v644yvm4qjag5l5ri5lq7ztwee

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

2020 IEEE transactions on circuits and systems for video technology (Print)  
Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network.  ...  ., +, TCSVT March 2020 711-724 Camera Geometric Calibration Using Dynamic Single-Pixel Illumination With Deep Learning Networks.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu


2019 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA)  
Three-stream Very Deep Neural Network for Video Action Recognition Unsupervised Hyperspectral Target Detection Using Spectral Residual of Deep Autoencoder Networks Towards Information Theoretic Measurement  ...  in video Defect detection in metallic structures through AMR C-scan images using deep learning method.  ... 
doi:10.1109/pria.2019.8785061 fatcat:e43b5ycj2jfybk4ozrvqzhsk4a

Video Classification Using Deep Learning

Sheshang Degadwala, Harsh Parekh, Nirav Ghodadra, Harsh Chauhan, Mashkoor Hussaini
2020 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
We have driven wide assessment on four going after for video arrangement including human movement affirmation and dynamic scene grouping.  ...  With the phenomenal accomplishment of significant learning, convolutional neural frameworks (CNNs) and their 3-D varieties have been considered in the video territory for an immense grouping of order assignments  ...  Jothilakshmi portrays Crowd Video Event Classification Using Convolution Neural Network. In that they utilized Deep Learning, CNN, SVM and Deep Neural Network.  ... 
doi:10.32628/cseit2062134 fatcat:spnsm4ea45bq3kcsyevze2nxlu

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 6561-6573 A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections.  ...  ., +, TIP 2020 6194-6208 Dynamic Sampling Networks for Efficient Action Recognition in Videos.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Embedded Motion Detection via Neural Response Mixture Background Modeling

Mohammad Javad Shafiee, Parthipan Siva, Paul Fieguth, Alexander Wong
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
The proposed Neural Response Mixture (NeRM) model leverages rich deep features extracted from the neural responses of an efficient, stochastically-formed deep neural network (Stochas-ticNet) for constructing  ...  Recent studies have shown that deep neural networks (DNNs) can outperform state-of-the-art algorithms for a multitude of computer vision tasks.  ...  This new approach can open a new avenue to facilitate the use of deep neural networks on embedded systems which has huge applicability in dif- Shadow Dynamic Background Low Frame Rate Night Videos  ... 
doi:10.1109/cvprw.2016.109 dblp:conf/cvpr/ShafieeSFW16 fatcat:qlb4tvtrffcadjiu34dxsnf6hq
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