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Soft Computing

, Volume 22, Issue 1, pp 231–242 | Cite as

Group object detection and tracking by combining RPCA and fractal analysis

  • Longxin Lin
  • Weiwei Lin
  • Sibin Huang
Methodologies and Application

Abstract

Automatic video analysis is a hot research topic in the field of computer vision and has broad application prospects. It usually consists of three key steps: object detection, object tracking and behavior recognition. Usually, object detection is just considered as the precondition of object tracking, and the correlation between them is very little. So, existing video analysis solutions treat them as independent procedures and execute them separately. Actually, object detection and tracking are related and the effective combination of them can improve the performance of video analysis. This paper mainly studies object detection and tracking, and tries to utilize the outputs of them to optimize their performance by each other. For this purpose, a unified algorithm framework called group object detection and tracking is presented, which detects moving objects by robust principle component analysis (RPCA) and Graph Cut algorithm and tracks objects via fractal analysis simultaneously. The multi-fractal spectrum (MFS) constrain and Graph Cut improve the complement of object detection, which will bring more exact tracking feature. At the same time, the successful results from tracking can provide optimal constrain for object detection in an opposite manner. Therefore, object detection and tracking are grouped and can be improved by an iterative RPCA algorithm. The experimental results of simulation and real sequence demonstrate that the proposed algorithm is more robust and outperforms state-of-art algorithms in object detection and tracking.

Keywords

Fractal analysis Robust principle component analysis Object detection Object tracking Motion segmentation Multi-fractal spectrum 

Notes

Acknowledgments

We want to thank the helpful comments and suggestions from the anonymous reviewers. This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61501207 and 61402183), Guangdong Natural Science Foundation (Grant Nos. S2012030006242 and S2013040012449), Guangdong Provincial Scientific and Technological Projects (Grant Nos. 2013B090500030, 2016A010119171, 2016A010101018, 2016A010101007, and 2016B090918021), Guangzhou Scientific and Technological Projects (Grant Nos. 2013Y2-00065, 2013J4300056, 2014Y2-00133, 201601010314, 201607010048 and 201604010040).

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.College of Information Science and TechnologyJinan UniversityGuangzhouChina
  2. 2.School of Computer Engineering and ScienceSouth China University of TechnologyGuangzhouChina
  3. 3.Guangzhou Pixcoo Information and Technology LTDGuangzhouChina

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