Dynamic Compressive Tracking

Ting Chen, Yanning Zhang, Tao Yang, Hichem Sahli

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

4 Citations (Scopus)


The original real-time compressive tracking use a random
matrix to get the appearance model based on features extracted
form the image feature space performs well in most
scene. However, when the object is low-grain, low-resolution,
or small, a fixed size sparse measurement matrix is not sufficient
enough to preserve the structure of the image of the
object. In this work, we propose a Dynamic Compressive
Tracking algorithm that employs adaptive random projections
that preserve the structure of the image feature space
of objects during tracking. The proposed tracker automatically
estimates and ranks the amount of random feature projections
of the object in the compressive domain. Extensive
experimental results, on challenging public available data
sets shows, that the proposed dynamic compressible tracking
algorithm outperforms conventional compressive tracker,
and it is comparable to the state-of-the-art tracking methods.
Original languageEnglish
Title of host publicationProceedings of International Conference on Advances in Mobile Computing & Multimedia(MoMM '13)
Number of pages7
Publication statusPublished - 2013
EventInternational Conference on Advances in Mobile Computing & Multimedia - Vienna, Austria
Duration: 2 Dec 20134 Dec 2013


ConferenceInternational Conference on Advances in Mobile Computing & Multimedia
Abbreviated titleMoMM2013


  • Scene analysis,
  • tracking

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