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Online, Real-Time Tracking Using a Category-to-Individual Detector

  • David Hall
  • Pietro Perona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8689)

Abstract

A method for online, real-time tracking of objects is presented. Tracking is treated as a repeated detection problem where potential target objects are identified with a pre-trained category detector and object identity across frames is established by individual-specific detectors. The individual detectors are (re-)trained online from a single positive example whenever there is a coincident category detection. This ensures that the tracker is robust to drift. Real-time operation is possible since an individual-object detector is obtained through elementary manipulations of the thresholds of the category detector and therefore only minimal additional computations are required. Our tracking algorithm is benchmarked against nine state-of-the-art trackers on two large, publicly available and challenging video datasets. We find that our algorithm is 10% more accurate and nearly as fast as the fastest of the competing algorithms, and it is as accurate but 20 times faster than the most accurate of the competing algorithms.

Keywords

Target Object Tracking Algorithm Appearance Model Pedestrian Detection Individual Detector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

978-3-319-10590-1_24_MOESM1_ESM.pdf (133 kb)
Electronic Supplementary Material (PDF 134 KB)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David Hall
    • 1
  • Pietro Perona
    • 1
  1. 1.California Institute of TechnologyUSA

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