文章基本信息

标题：Diverse Methodologies to Improve Covariance based Object Tracking
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作者：Sanchita Singha ; Sujoy Datta
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2015
卷号：8
期号：6
页码：33-44
DOI：10.14257/ijsip.2015.8.6.05
出版社：SERSC
摘要：Tracking is an important process of computer vision research. But still after so many researches accuracy is still become a bottleneck. Within different tracking techniques covariance based tracking is a new technique which gives more accuracy than other techniques. There are several methods and researches have been done on covariance tracking. The covariance tracking process also uses some distance measures to calculate the dissimilarity between two target regions. Here we have list down some of the most useful distance measurement techniques which provide accurate results. We have also implemented those distance measurement techniques and shown their results with accuracy comparison. Even the distance between the target and the candidate covariance matrix is itself enough track an object, but to get more accurate result some techniques are applied on covariance tracking. Here we have listed some of those techniques which happen to provide better results after applying on covariance tracking and also pointed out the advantages and drawbacks of those techniques
关键词：Covariance; manifold; Euclidian space; kernel; salient feature