Robust object tracking via adaptive sparse representation

Rahman Khorsandi, Mohamed Abdel-Mottaleb

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we present a robust object tracking system capable of handling pose and scale variations. The system is based on adaptive sparse representation and dictionary learning. We focus on the problem of automatic tracking with no prior knowledge other than the location of the region to be tracked in the first frame, which could be located by a detector. The detected region, i.e., a bounding box, and some samples near the bounding box are extracted as positive samples. In addition, we select regions outside the bounding box as negative samples. Both, positive and negative samples are used to build the dictionary and we use K-SVD method for dictionary learning in order to decrease the number of atoms and improve the processing speed. One of the main drawbacks in tracking systems is false tracking when the object is not in the frame any more. We overcome this problem by comparing the newly tracked region with previously tracked regions to find out if the object is still in the frame or not. If the object is not in the frame, the algorithm stops tracking and starts searching for the object using the sparse detector in the following frames. Experiments on video sequences demonstrate the effectiveness and robustness of the proposed system for tracking.

Original languageEnglish (US)
Title of host publication2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781479975914
StatePublished - Feb 23 2016
EventIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States
Duration: Dec 13 2015Dec 16 2015


OtherIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
Country/TerritoryUnited States


  • Dictionary Learning
  • Object Detection
  • Sparse Representation
  • Tracking

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing


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