Abstract:
It's an arduous task to write an effcx: tivc and e ffic ient a lgorithm for robust object tracking. Object
tracking depends on factors like variation in pose, motion blurring, change in illumination and
occlusion. Many objects tracking a lgori thms monitoring samples in previous frames and update
the models. While accomplishment has been achieved, still many issues remain needed to be
entertained. Firstly, adaptive appearance algori thms are dependent on data, and a heavy amount
of informmion is needed to learn for online algorithms at the outset. Secondly, drift problem is
experienced by online tracking algorithms while detecting the o bject in fa st motion. Miss-a ligned
samples are added which reduce e rlicicncy and accuracy o f appearance model caused by selftaught
learning. In this research. I propose an elementary robust object tracking algorithm by
using an appearance model. This model based on features which can be obtained by an image
feature space on the basis of data -independent, whjch is more effect ive and efficient. This model
uses no adapt ive random projections, which usually retain the stnJcture of an object. To extract
features accurately and efficiently for an appea rance model, sparse measurement matrix is used
for it. TIle foreground and background samples of targel object compress with the help o f sparse
measurement matrix. The target objcct is tracked by Nai've Bayes classi fier for binary
classification by compressing. The proposed o bject tracking algorithm is compress ive and
perfonns favorably well 1Il a mean of efficiency. accuracy. and robustness.
