Real-Time Moving Object Detection in High-Resolution Video Sensing
暂无分享,去创建一个
Nasser Kehtarnavaz | Baoqing Li | Haoran Wei | Xiaobing Yuan | Haidi Zhu | N. Kehtarnavaz | Haoran Wei | Haidi Zhu | Baoqing Li | Xiaobing Yuan
[1] Yinkun Bai. Target Detection Method of Underwater Moving Image Based on Optical Flow Characteristics , 2019 .
[2] Subrahmanyam Murala,et al. MSFgNet: A Novel Compact End-to-End Deep Network for Moving Object Detection , 2019, IEEE Transactions on Intelligent Transportation Systems.
[3] Chung-Cheng Chiu,et al. Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees , 2015, Sensors.
[4] Lei Wang,et al. Moving vehicle detection based on fuzzy background subtraction , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[5] Sandeep Singh Sengar,et al. Moving object detection based on frame difference and W4 , 2017, Signal Image Video Process..
[6] Kenji Suzuki,et al. A Run-Based Two-Scan Labeling Algorithm , 2008, IEEE Transactions on Image Processing.
[7] Kesheng Wu,et al. Optimizing two-pass connected-component labeling algorithms , 2009, Pattern Analysis and Applications.
[8] En Li,et al. Apple detection during different growth stages in orchards using the improved YOLO-V3 model , 2019, Comput. Electron. Agric..
[9] Hanqing Lu,et al. Pixelwise Deep Sequence Learning for Moving Object Detection , 2019, IEEE Transactions on Circuits and Systems for Video Technology.
[10] Muhammad Omer Farooq,et al. Pedestrian Detection in Infrared Images Using Fast RCNN , 2018, 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA).
[11] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[12] Satrughan Kumar,et al. Segmentation of Moving Object Using Background Subtraction Method in Complex Environments , 2016 .
[13] Nasser Kehtarnavaz,et al. Semi-Supervised Faster RCNN-Based Person Detection and Load Classification for Far Field Video Surveillance , 2019, Mach. Learn. Knowl. Extr..
[14] Xiaoqiang Shao,et al. An Improved Moving Target Detection Method Based on Vibe Algorithm , 2018, 2018 Chinese Automation Congress (CAC).
[15] Sandeep Singh Sengar,et al. Detection of moving objects based on enhancement of optical flow , 2017 .
[16] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] R. Arokia Priya,et al. Detecting Moving Object Using Background Subtraction Algorithm in FPGA , 2014, 2014 Fourth International Conference on Advances in Computing and Communications.
[18] Baoqing Li,et al. Moving Object Detection With Deep CNNs , 2020, IEEE Access.
[19] Bin Yao,et al. An efficient two-scan algorithm for computing basic shape features of objects in a binary image , 2016, Journal of Real-Time Image Processing.
[20] Gerhard Rigoll,et al. A deep convolutional neural network for video sequence background subtraction , 2018, Pattern Recognit..
[21] Ali Farhadi,et al. YOLOv3: An Incremental Improvement , 2018, ArXiv.
[22] Wenchao Huang,et al. An improved frame difference method for moving target detection , 2017, 2017 Chinese Automation Congress (CAC).
[23] Lin Teng,et al. Improved background subtraction method for detecting moving objects based on GMM , 2018, IEEJ Transactions on Electrical and Electronic Engineering.
[24] Xiaobo Lu,et al. WeSamBE: A Weight-Sample-Based Method for Background Subtraction , 2018, IEEE Transactions on Circuits and Systems for Video Technology.
[25] Nuno Vasconcelos,et al. Cascade R-CNN: Delving Into High Quality Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Bilal Bataineh. A fast and memory-efficient two-pass connected-component labeling algorithm for binary images , 2019 .
[28] Jianfang Dou,et al. Moving object detection based on improved VIBE and graph cut optimization , 2013 .
[29] Zhiming Luo,et al. Interactive deep learning method for segmenting moving objects , 2017, Pattern Recognit. Lett..
[30] Ming Zhu,et al. Background Subtraction Using Multiscale Fully Convolutional Network , 2018, IEEE Access.
[31] Guillaume-Alexandre Bilodeau,et al. SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity , 2015, IEEE Transactions on Image Processing.
[32] N. Zarrinpanjeh,et al. Using GLCM features in Haar wavelet transformed space for moving object classification , 2019 .
[33] Ali Farhadi,et al. YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[35] Menglong Yan,et al. Ground Moving Target Indication Based on Optical Flow in Single-Channel SAR , 2019, IEEE Geoscience and Remote Sensing Letters.
[36] Arnav Bhavsar,et al. Localization of common carotid artery transverse section in B-mode ultrasound images using faster RCNN: a deep learning approach , 2020, Medical & Biological Engineering & Computing.
[37] Jian Wang,et al. Feature‐based detection and classification of moving objects using LiDAR sensor , 2019, IET Intelligent Transport Systems.
[38] Bing Tu,et al. Moving Object Detection Method via ResNet-18 With Encoder–Decoder Structure in Complex Scenes , 2019, IEEE Access.
[39] Hui Liu,et al. Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm , 2020, Sensors.
[40] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Federico Bolelli,et al. Optimized Block-Based Algorithms to Label Connected Components on GPUs , 2020, IEEE Transactions on Parallel and Distributed Systems.
[42] Jaeseok Kim,et al. Block-Based connected component labeling algorithm with block prediction , 2017, 2017 40th International Conference on Telecommunications and Signal Processing (TSP).
[43] Guoxu Liu,et al. YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3 , 2020, Sensors.