T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Publication in refereed journal

替代計量分析
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其它資訊
摘要The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks, such as GoogleNet and VGG, novel object detection frameworks, such as R-CNN and its successors, Fast R-CNN, and Faster R-CNN, play an essential role in improving the state of the art. Despite their effectiveness on still images, those frameworks are not specifically designed for object detection from videos. Temporal and contextual information of videos are not fully investigated and utilized. In this paper, we propose a deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing still-image detection frameworks when they are applied to videos. It is called T-CNN, i.e., tubelets with convolutional neural networks. The proposed framework won newly introduced an object-detection-from-video task with provided data in the ImageNet Large-Scale Visual Recognition Challenge 2015. Code is publicly available at https://github.com/myfavouritekk/T-CNN.
出版社接受日期07.08.2017
著者Kai Kang, Hongsheng Li, Junjie Yan, Xingyu Zeng, Bin Yang, Tong Xiao, Cong Zhang, Zhe Wang, Ruohui Wang, Xiaogang Wang, Wanli Ouyang
期刊名稱IEEE Transactions on Circuits and Systems for Video Technology
出版年份2018
月份10
卷號28
期次10
頁次2896 - 2907
國際標準期刊號1051-8215
電子國際標準期刊號1558-2205
語言美式英語

上次更新時間 2021-17-10 於 23:50