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Domain adaption of vehicle detector based on convolutional neural networks

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  • Intelligent and Information Systems
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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.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Center for Robotics and Key Laboratory for Neuro Information of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China

    Xudong Li, Mao Ye, Min Fu, Pei Xu & Tao Li

Authors
  1. Xudong Li
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  2. Mao Ye
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  3. Min Fu
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  4. Pei Xu
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  5. Tao Li
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Corresponding author

Correspondence to Mao Ye.

Additional information

Recommended by Associate Editor Dong-Joong Kang under the direction of Editor Euntai Kim.

This work was supported in part by the National Natural Science Foundation of China (61375038) and the Academic Support Program for Excellent Doctors (YBXSZC20131064).

Xudong Li received his B.S. degree in Mathematics from Chengdu University of Technology, Chengdu, China, in 2011. He has been taking the successive master-doctor program since September 2011. He is currently a Ph.D. student in University of Electronic Science and Technology of China, Chengdu, China. His current research interests include machine learning and computer vision.

Mao Ye received his Ph.D. degree in Mathematics from Chinese University of Hong Kong, in 2002. He is currently a professor and Director of CVLab at University of Electronic Science and Technology of China. His current research interests include machine learning and computer vision. In these areas, he has published over 70 papers in leading international journals or conference proceedings.

Min Fu received her bachelor degree from Southwest University of Science and Technology, Mianyang, China, in 2011. As a graduate student she engaged in machine learning and image processing at University of Electronic Science and Technology of China, Chengdu, China and she received her master degree in 2014.

Pei Xu received his B.S. degree in Computer Science and Technology from Si- Chuan University of Science and Engineering, ZiGong, China, in 2008 and his MS degree in condensed matter physics from University of Electronic Science and Technology of China, Chengdu, China, in 2011. He is currently a Ph.D. student in University of Electronic Science and Technology of China, Chengdu, China. His current research interests include machine learning and computer vision.

Tao Li received his M.E. degree from Central South University, Changsha, China in 2006. He is now a Ph.D. student in University of Electronic Science and Technology, Chengdu, China. His current research interests are machine vision, visual surveillance and object detection.

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Li, X., Ye, M., Fu, M. et al. Domain adaption of vehicle detector based on convolutional neural networks. Int. J. Control Autom. Syst. 13, 1020–1031 (2015). https://doi.org/10.1007/s12555-014-0119-z

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  • DOI: https://doi.org/10.1007/s12555-014-0119-z

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