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Unsupervised tracking, roughness and quantitative indices

Pal, Sankar K.; Chakraborty, Debarati


Sankar K. Pal

Debarati Chakraborty


This paper presents a novel methodology for tracking a single moving object in a video sequence applying the concept of rough set theory. The novelty of this technique is that it does not consider any prior information about the video sequence unlike many existing techniques. The first target model is constructed using the median filtering based foreground detection technique and after that the target is reconstructed in every frame according to the rough set based feature reduction concept incorporating a measure of indiscernibility instead of indiscernibility matrix. The area of interest is initially defined roughly in every frame based on the object shift in the previous frames, and after reduction of redundant features the object is tracked. The measure of indiscernibility of a feature is defined based on its degree of belonging (DoB) to the target. Three quantitative indices based on rough sets, feature similarity and Bhattacharya distance are proposed to evaluate the performance of tracking and detect the mis-tracked frames in the process of tracking to make those corrected. Unlike many existing measures, the proposed ones do not require to know the ground truth or trajectory of the video sequence. Extensive experimental results are given to demonstrate the effectiveness of the method. Comparative performance is demonstrated both visually and quantitatively.


Pal, S. K., & Chakraborty, D. (2013). Unsupervised tracking, roughness and quantitative indices. Fundamenta Informaticae, 124(1-2), 63-90.

Journal Article Type Article
Acceptance Date Mar 5, 2013
Publication Date Jul 3, 2013
Deposit Date Mar 13, 2024
Journal Fundamenta Informaticae
Print ISSN 0169-2968
Publisher IOS Press
Peer Reviewed Peer Reviewed
Volume 124
Issue 1-2
Pages 63-90
Keywords Rough Set; Unsupervised Tracking; Feature Reduction; Bhattacharya distance; Moving object segmentation
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