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Automatic object classification using motion blob based local feature fusion for traffic scene surveillance

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Abstract

Automatic object classification in traffic scene videos is an important issue for intelligent visual surveillance with great potential for all kinds of security applications. However, this problem is very challenging for the following reasons. Firstly, regions of interest in videos are of low resolution and limited size due to the capacity of conventional surveillance cameras. Secondly, the intra-class variations are very large due to changes of view angles, lighting conditions, and environments. Thirdly, real-time performance of algorithms is always required for real applications. In this paper, we evaluate the performance of local feature descriptors for automatic object classification in traffic scenes. Image intensity or gradient information is directly used to construct effective feature vectors from regions of interest extracted via motion detection. This strategy has great advantages of efficiency compared to various complicated texture features. We not only analyze and evaluate the performance of different feature descriptors, but also fuse different scales and features to achieve better performance. Numerous experiments are conducted and experimental results demonstrate the efficiency and effectiveness of this strategy with robustness to noise, variance of view angles, lighting conditions, and environments.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Zhaoxiang Zhang & Yunhong Wang

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  1. Zhaoxiang Zhang
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  2. Yunhong Wang
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Correspondence to Zhaoxiang Zhang.

Additional information

Zhaoxiang Zhang received his BS in Electronic Science and Technology from the University of Science and Technology of China, Hefei, China, in 2004. He received his PhD in Pattern Recognition and Intelligent Systems from the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2009. In October 2009, he joined the Laboratory of Intelligent Recognition and Image Processing, Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, as a Lecturer. His research interests include computer vision, pattern recognition, image processing, and machine learning.

Yunhong Wang received her BS in Electronic Engineering from Northwestern Polytechnical University, Xi’an, China, in 1989 and her MS and PhD in Electronic Engineering from Nanjing University of Science and Technology, Nanjing, China, in 1995 and 1998, respectively. From 1998 to 2004, she was with the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China. Since 2004, she has been a professor with the School of Computer Science and Engineering, Beihang University, Beijing, where she is also the Director of the Laboratory of Intelligent Recognition and Image Processing, Beijing Key Laboratory of Digital Media. Her research interests include biometrics, pattern recognition, computer vision, data fusion, and image processing.

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Zhang, Z., Wang, Y. Automatic object classification using motion blob based local feature fusion for traffic scene surveillance. Front. Comput. Sci. 6, 537–546 (2012). https://doi.org/10.1007/s11704-012-1296-7

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