Open AccessSparse Representation Based Multi Object Tracking using GPU
Author(s) -
Anuja Kumar Acharya,
Rajalakshmi Satapathy,
Biswajit Sahoo
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b1055.1292s19
Subject(s) - computer science , sparse approximation , matching pursuit , artificial intelligence , object (grammar) , representation (politics) , overhead (engineering) , feature (linguistics) , video tracking , pattern recognition (psychology) , set (abstract data type) , cuda , computer vision , matching (statistics) , parallel computing , mathematics , compressed sensing , linguistics , philosophy , statistics , politics , political science , law , programming language , operating system
This work proposes a sparse based representation for tracking multi object for the longer sequence of video frame. Object of interest are first identified and then represented with set of low dimensional feature covariance matrix. These feature of different object are kept in a dictionary. In order to classify the object, sparse based Orthogonal matching pursuit(OMP) algorithm is used. Furthermore, towards reducing the computational overhead, proposed model is implemented on a graphical processing unit enhanced with the multi threaded resource for parallelization of the task. Experimental results shows that proposed method out perform as compared with the state of art in identifying the objects.