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Visual Distinctiveness Detection of Pedestrian based on Statistically Weighting PLSA for Intelligent Systems

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  • Robot and Applications
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Abstract

Intelligent Systems for autonomous vehicles including drone, robot vision, and video surveillance, need to distinguish pedestrian from other object. Pedestrian detection is an essential and significant research topic due to its diverse applications. In this paper, a new visual distinctiveness detection method for pedestrian is proposed based on the statistically weighting probabilistic latent semantic analysis. We detect the distinctiveness by integrating three steps as follows: first representing the co-ocurrence matrix of images, which were vectorized using the bag of visual words (BoVW) framework; then calculating the weights through the histograms of visual words of each class; and finally applying the weights to the test images as the distinctiveness of visual words. The probabilistic latent semantic analysis (PLSA) was used as classification method in our system. We extracted the weighted visual words by sampling the patches from the current image. The proposed method was compared to the PLSA using the Caltech 256 datasets. The classes used include pedestrians, cars, motorbikes, airplanes and horses. The results of the experiment show that the proposed method outperforms current methods in predicting pedestrians and transportation objects.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Chung-Ang University, 84 Heukseok-Ro, Dongjak-Gu, Seoul, Korea

    Hyun Chul Song, Gyun Hyuk Lee & Kwang Nam Choi

  2. School of Electrical and Electronics Engineering, Chung-Ang University, 84 Heukseok-Ro, Dongjak-Gu, Seoul, Korea

    Duk-Sun Shim

Authors
  1. Hyun Chul Song
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  2. Gyun Hyuk Lee
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  3. Duk-Sun Shim
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  4. Kwang Nam Choi
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Corresponding author

Correspondence to Kwang Nam Choi.

Additional information

Recommended by Associate Editor Sung Jin Yoo under the direction of Editor Euntai Kim. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2010-0025512). This research was supported by the Chung-Ang University Research Scholarship Grants in 2017.

Hyun Chul Song is a Ph.D. candidate of the School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea. His research interests include image classification, image captioning, and generative adversarial networks.

Gyun Hyuk Lee is a M.S. student of the School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea. His research interests include haze removal recognition.

Duk-Sun Shim is a professor at the School of Electrical and Electronics Engineering in Chung-Ang University, Seoul, Korea. He received his B.S. and M.S. degrees from the Department of Control and Instrumentation Engineering in Seoul National University, Seoul, Korea, in 1984 and 1986, respectively. He received his Ph.D. degree in Aerospace Engineering from the University of Michigan, Ann Arbor, USA, in 1993. He served at the Department of Electrical Engineering and Computer Science in the University of Michigan as a postdoc from January 1994 to January 1995. Since March 1995, he has been with the School of Electrical and Electronics Engineering in Chung-Ang University, Seoul, Korea, where he is currently a Professor. His research interests include robust control, robot SLAM, GNSS and inertial navigation systems, fault detection and isolation, and computer vision.

Kwang Nam Choi is a professor at the School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea. He received his B.S. and M.S. degrees from the Department of Computer Science in Chung-Ang University, Korea, in 1988 and 1990, respectively. He received his Ph.D. degree in Computer Science from the University of York, U.K. in 2002. Since 2002, he has been with the School of Computer Science and Engineering in Chung-Ang University, Seoul, Korea where he is currently a Professor. His research interests include motion tracking, object categorization, and 3D image recognition.

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Song, H.C., Lee, G.H., Shim, DS. et al. Visual Distinctiveness Detection of Pedestrian based on Statistically Weighting PLSA for Intelligent Systems. Int. J. Control Autom. Syst. 16, 815–822 (2018). https://doi.org/10.1007/s12555-017-0253-5

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  • DOI: https://doi.org/10.1007/s12555-017-0253-5

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