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Journal of Computer Science and Technology

, Volume 30, Issue 2, pp 364–372 | Cite as

Raw Trajectory Rectification via Scene-Free Splitting and Stitching

  • Chun-Chao Guo
  • Xiao-Jun Hu
  • Jian-Huang LaiEmail author
  • Shi-Chang Shi
  • Shi-Zhe Chen
Regular Paper

Abstract

Trajectories carry rich motion cues and thus have been leveraged to many high-level computer vision tasks. Due to the easy implementation of simple trackers, most previous work on trajectory-based applications utilizes raw tracking outputs without explicitly considering tracking errors. Reliable trajectories are prerequisite for modeling and recognizing high-level behaviors. Therefore, this paper tackles such problems by rectifying raw trajectories, which aims to post-process existing trajectories. Our approach firstly splits them into short tracks, and then infers identity ambiguity to remove unqualified detection responses. At last, short tracks are stitched via maximum bipartite graph matching. This postprocessing is completely scene-free. Results of trajectory rectification and their benefits are both evaluated on two challenging datasets. Results demonstrate that rectified trajectories are conducive to high-level tasks and the proposed approach is also competitive with state-of-the-art multi-target tracking methods.

Keywords

raw trajectory rectification trajectory post-processing identity ambiguity multi-target tracking activity classification 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Chun-Chao Guo
    • 1
  • Xiao-Jun Hu
    • 1
  • Jian-Huang Lai
    • 1
    • 2
    Email author
  • Shi-Chang Shi
    • 1
  • Shi-Zhe Chen
    • 1
  1. 1.School of Information Science and TechnologySun Yat-sen UniversityGuangzhouChina
  2. 2.SYSU-CMU Shunde International Joint Research InstituteShundeChina

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