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Fast object detection based on several samples by training voting space

  • Mathematical Method in Pattern Recognition
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Abstract

In this paper, we propose a fast and novel detection method based on several samples to localize objects in target images or video. Firstly, we use several samples to train a voting space which is constructed by cells at corresponding positions. Each cell is described by a Gaussian distribution whose parameters are estimated by maximum likelihood estimation method. Then, we randomly choose one sample as a query image. Patches of target image are recognized by densely voting in the trained voting space. Next, we use a mean-shift method to refine multiple instances of object class. The high performance of our approach is demonstrated on several challenging data sets in both efficiency and effectiveness.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, School of Computer Science and Engineering, Chengdu, China

    Pei Xu, Mao Ye, Lishen Pei, Yumin Dou & Hongyi Chen

Authors
  1. Pei Xu
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  2. Mao Ye
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  3. Lishen Pei
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  4. Yumin Dou
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  5. Hongyi Chen
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Correspondence to Pei Xu.

Additional information

The article is published in the original.

Pei Xu received his BS degree in computer science and technology from Si Chuan University of Science and Engineering, Zi Gong, 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 PhD 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 PhD 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.

Hongyi Chen received his BS degree in mathematics from University of Electronic Science and Technology of China, Chengdu, China, in 2011. He is currently a postgraduate student in the University of Electronic Science and Technology of China, Chengdu, China. His current research interests are machine vision, visual surveillance, and object detection.

Lishen Pei received her BS degree in computer science and technology from Anyang Teachers College, Anyang, China, in 2010. She is currently an MS student in the University of Electronic Science and Technology of China, Chengdu, China. Her current research interests include action detection and action recognition in computer vision.

Yumin Dou received his master’s degree in Computer Science from Chengdu University of Technology in 2010. He is now a Ph.D. candidate at the School of Computer Science and Engineering of University of Electronic Science and Technology University of China. His research interests include pattern recognition and computer vision.

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Xu, P., Ye, M., Pei, L. et al. Fast object detection based on several samples by training voting space. Pattern Recognit. Image Anal. 25, 565–576 (2015). https://doi.org/10.1134/S1054661815040227

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  • DOI: https://doi.org/10.1134/S1054661815040227

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