NDLI: A new fast object detection architecture combing manually-designed feature and CNN

Content Provider	IEEE Xplore Digital Library
Author	Zhang, Haiyang Wan, Shouhong Yue, Lihua Wu, Zhize Zhao, Yunhao
Copyright Year	2015
Description	Author affiliation: Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences School of Computer Science and Technology, University of Science and Technology of China, Hefei, China (Zhang, Haiyang; Wan, Shouhong; Yue, Lihua; Wu, Zhize; Zhao, Yunhao)
Abstract	We proposed a new fast object detection architecture based on region, which consists of two stages, using manually-designed features and convolutional neural network (CNN) respectively. The first stage is generating many initial proposal windows, and then do objectness measure and ranking, to reduce the quantity of proposal windows. In this stage, we apply four manually-designed objectness features that take much less time to compute. To do the ranking, we apply methods based on ranking SVM and non-max suppression. The second stage is that Deep CNN classifies the candidate windows into special-category and makes the final detection results. Since every window's CNN features computation time consumption is very expensive, we reduce the large set of initial proposal window in an image to small set of candidate windows to reduce the total time of computing CNN features of an image, and thus save the detection run-time. The experiment shows our proposed method achieved the comparable result on VOC 2007 with the state-of-the-art with about half time consumption.
Starting Page	572
Ending Page	577
File Size	418898
Page Count	6
File Format	PDF
e-ISBN	9781467390989
DOI	10.1109/CISP.2015.7407944
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	CNN Image color analysis Computer architecture Object detection Manuals Detectors Feature extraction cascade filter object detection Proposals
Content Type	Text
Resource Type	Article

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