|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'506'133
Articles rated: 2609
26 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos | | Kai Kang
; Hongsheng Li
; Junjie Yan
; Xingyu Zeng
; Bin Yang
; Tong Xiao
; Cong Zhang
; Zhe Wang
; Ruohui Wang
; Xiaogang Wang
; Wanli Ouyang
; | | Date: |
9 Apr 2016 | | Abstract: | 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 work, 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 neueral networks. The
proposed framework won the recently introduced object-detection-from-video
(VID) task with provided data in the ImageNet Large-Scale Visual Recognition
Challenge 2015 (ILSVRC2015). | | Source: | arXiv, 1604.2532 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER