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Tracking video objects with feature points based particle filtering

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Abstract

For intelligent video surveillance, the adaptive tracking of multiple moving objects is still an open issue. In this paper, a new multi-object tracking method based on video frames is proposed. A type of particle filtering combined with the SIFT (Scale Invariant Feature Transform) is proposed for motion tracking, where SIFT key points are treated as parts of particles to improve the sample distribution. Then, a queue chain method is adopted to record data associations among different objects, which could improve the detection accuracy and reduce the computational complexity. By actual road tests and comparisons, the system tracks multi-objects with better performance, e.g., real time implementation and robust against mutual occlusions, indicating that it is effective for intelligent video surveillance systems.

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Notes

  1. All sequences can be download form http://www.umiacs.umd.edu/~knkim/UMD-BGS/index.html#Download

  2. All sequences can be download form http://www.cvg.cs.rdg.ac.uk/cgi-bin/PETSMETRICS/page.cgi?dataset.

  3. All sequences can be download form http://i2lwww.ira.uka.de/image_sequences

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Author information

Authors and Affiliations

  1. Electronic Information Products Supervision and Inspection Institute of Hebei Province, Shijiazhuang, 050071, China

    Tao Gao

  2. Industry and Information Technology Department of Hebei Province, Shijiazhuang, 050051, China

    Tao Gao

  3. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Guo Li

  4. France Telecom (Orange Labs) Beijing, Beijing, 100080, China

    Shiguo Lian

  5. School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, China

    Jun Zhang

Authors
  1. Tao Gao
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  2. Guo Li
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  3. Shiguo Lian
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  4. Jun Zhang
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Corresponding author

Correspondence to Tao Gao.

Additional information

This work is supported by the National Science Foundation of China under Grant No. 70773008, Program for New Century Excellent Talents in University under Grant No. NCET-10-0048, Fok Ying Tung Education Foundation under Grant No. 121079, and the Science Foundations of Tianjin under Grant No. 10ZCKFSF01100.

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Gao, T., Li, G., Lian, S. et al. Tracking video objects with feature points based particle filtering. Multimed Tools Appl 58, 1–21 (2012). https://doi.org/10.1007/s11042-010-0676-y

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