Cascaded regional spatio-temporal feature-routing networks for video object detection
Author(s)
Shuai, Hui
Liu, Qingshan
Zhang, Kaihua
Yang, Jing
Deng, Jiankang
Type
Journal Article
Abstract
This paper presents a cascaded regional spatiotemporal feature-routing networks for video object detection. Region proposal networks in faster region-based convolutional neural network (CNN) generate spatial proposals, whereas neglecting the temporal property of the videos. We incorporate the correlation filter tracking on the convolutional feature maps to explore an efficient and effective spatiotemporal region proposal generation method. To gradually refine the bounding boxes of proposals, three region classification and regression networks are cascaded. Feature maps from different layers in CNNs extract hierarchical information of the input, so we propose a router function which selects feature maps according to the scale of proposals. In addition, object co-occurrence inference is exploited to suppress conflicting false positives, which leads to a semantically coherent interpretation on the video. Extensive experiments on the Pascal VOC 2007 dataset and the ImageNet VID dataset show that the proposed method achieves the state-of-the-art performance for detecting unconstrained objects in cluttered scenes.
Date Issued
2018-02-14
Date Acceptance
2017-12-12
Citation
IEEE Access, 2018, 6, pp.3096-3106
ISSN
2169-3536
Publisher
IEEE
Start Page
3096
End Page
3106
Journal / Book Title
IEEE Access
Volume
6
Copyright Statement
© 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Subjects
Computer Science
Computer Science, Information Systems
co-occurrence inference
correlation filter tracking
Engineering
Engineering, Electrical & Electronic
regression networks
router-function
Science & Technology
Technology
Telecommunications
TRACKING
Video object detection
Publication Status
Published
Date Publish Online
2017-12-27