Digital video processing
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
A fuzzy logic approach for detection of video shot boundaries
Pattern Recognition
Implementation of Robot Visual Tracking System Based on Rough Set Theory
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Generalized rough sets, entropy, and image ambiguity measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Performance evaluation metrics and statistics for positional tracker evaluation
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Video Tracking: Theory and Practice
Video Tracking: Theory and Practice
Handbook on Soft Computing for Video Surveillance
Handbook on Soft Computing for Video Surveillance
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Generalized Kernel-Based Visual Tracking
IEEE Transactions on Circuits and Systems for Video Technology
Two Semantic Issues in a Probabilistic Rough Set Model
Fundamenta Informaticae - Advances in Rough Set Theory
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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.