Neighborhood rough filter and intuitionistic entropy in unsupervised tracking

Chakraborty, Debarati Bhunia; Pal, Sankar K.

Abstract

This paper aims at developing a novel methodology for unsupervised video tracking by exploring the merits of neighborhood rough sets. A neighborhood rough filter is designed in this process for initial labeling of continuous moving object(s) even in the presence of several variations in different feature spaces. The locations and color models of the object(s) are estimated using their lower-upper approximations in spatio-color neighborhood granular space. Velocity neighborhood granules and acceleration neighborhood granules are then defined over this estimation to predict the object location in the next frame and to speed up the tracking process. A novel concept, namely, intuitionsistic entropy is introduced here, which consists of two new measures: neighborhood rough entropy and neighborhood probabilistic entropy to deal with the ambiguities that arise due to occurrence of overlapping/ occlusion in a video sequence. The unsupervised method of tracking is equally good even when compared with some of the state-of-the art partially supervised methods while showing superior performance during total occlusion.

Citation

Chakraborty, D. B., & Pal, S. K. (2018). Neighborhood rough filter and intuitionistic entropy in unsupervised tracking. IEEE Transactions on Fuzzy Systems, 26(4), 2188-2200. https://doi.org/10.1109/TFUZZ.2017.2768322

• Journal Article Type: Article
• Online Publication Date: Nov 2, 2017
• Publication Date: Aug 1, 2018
• Deposit Date: Mar 13, 2024
• Journal: IEEE Transactions on Fuzzy Systems
• Print ISSN: 1063-6706
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Issue: 4
• Pages: 2188-2200
• DOI: https://doi.org/10.1109/TFUZZ.2017.2768322
• Keywords: Entropy; Granular computing; Internet-of-Things (IoT); Neighborhood granules; Object estimation; Occlusion handling; Rough filter; Rough sets; Unsupervised video tracking
• Public URL: https://hull-repository.worktribe.com/output/4588989