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Perceiving Motion From Dynamic Memory for Vehicle Detection in Surveillance Videos 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2019, 卷号: 29, 期号: 12, 页码: 3558-3567
作者:  Liu, Wei;  Liao, Shengcai;  Hu, Weidong
收藏  |  浏览/下载：282/0  |  提交时间：2020/03/30
Videos  Feature extraction  Object detection  Detectors  Surveillance  Proposals  Dynamics  Object detection  surveillance video  deep neural network  
Skeleton-Based Action Recognition With Gated Convolutional Neural Networks 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2019, 卷号: 29, 期号: 11, 页码: 3247-3257
作者:  Cao, Congqi;  Lan, Cuiling;  Zhang, Yifan;  Zeng, Wenjun;  Lu, Hanqing;  Zhang, Yanning
收藏  |  浏览/下载：289/0  |  提交时间：2020/03/30
Skeleton  Logic gates  Task analysis  Recurrent neural networks  Matrix converters  Three-dimensional displays  Convolutional neural networks  Skeleton  action recognition  gated connection  convolutional neural networks  
Multi-Modality Multi-Task Recurrent Neural Network for Online Action Detection 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2019, 卷号: 29, 期号: 9, 页码: 2667-2682
作者:  Liu, Jiaying;  Li, Yanghao;  Song, Sijie;  Xing, Junliang;  Lan, Cuiling;  Zeng, Wenjun
收藏  |  浏览/下载：228/0  |  提交时间：2019/12/16
Action detection  recurrent neural network  multi-modality  joint classification-regression  
Pixelwise Deep Sequence Learning for Moving Object Detection 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2019, 卷号: 29, 期号: 9, 页码: 2567-2579
作者:  Chen, Yingying;  Wang, Jinqiao;  Zhu, Bingke;  Tang, Ming;  Lu, Hanqing
收藏  |  浏览/下载：274/0  |  提交时间：2019/12/16
Moving object detection  background modeling  moving object proposal  convolutional neural networks  
Adversarial Deep Tracking 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2019, 卷号: 29, 期号: 7, 页码: 1998-2011
作者:  Zhao, Fei;  Wang, Jinqiao;  Wu, Yi;  Tang, Ming
浏览  |  Adobe PDF(6455Kb)  |  收藏  |  浏览/下载：306/104  |  提交时间：2019/04/30
Visual tracking  deep learning  adversarial training  attention