NDLI: Domain adaption of vehicle detector based on convolutional neural networks

Content Provider	Springer Nature Link
Author	Li, Xudong Ye, Mao Fu, Min Xu, Pei Li, Tao
Copyright Year	2015
Abstract	Generally the performance of a vehicle detector will decrease rapidly, when it is trained on a fixed training set but applied to a specific scene with view changes. The reason is that in the training set only a few samples are helpful for vehicle detection in the specific scene while other samples disturb the accurate detections. To solve this problem, we propose a novel transfer learning method to adapt the trained vehicle detector based on convolutional neural networks (ConvNets) to a specific scene with several new labeled samples. At first we reserve the share-filters and update the non-shared filters to improve the sensitivity of the vehicles in the specific scene. Then we combine the similar feature maps to accelerate the detection speed. At last for making the vehicle detector stable, we fine-tune it several times with the updated training set. Our contributions are an original research on transferring the vehicle detector based on ConvNets and an optimization approach about removing the redundant connections in the ConvNet vehicle detector. The extensive comparative experiments on three different datasets demonstrate that the transferred detectors achieve the improvements on both of the accuracy and speed.
Starting Page	1020
Ending Page	1031
Page Count	12
File Format	PDF
ISSN	15986446
Journal	International Journal of Control, Automation and Systems
Volume Number	13
Issue Number	4
e-ISSN	20054092
Language	English
Publisher	Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
Publisher Date	2015-05-23
Publisher Place	Bucheon, Seoul
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Convolutional neural networks domain adaption transfer learning vehicle detection Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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