@inproceedings{433af37470c84a07869849947b35ef5b,
title = "Visual tracking of a moving object via the soft cosine measure",
abstract = "In this paper, we propose a Local Soft Similarity based on Soft Cosine Measure (L3SCM) and then we incorporate it into visual tracking framework. Firstly, we present the soft cosine measure that measures the soft similarity between two vectors of features by taking into consideration similarities of pairs of features. Secondly, we apply this soft similarity in the observation model component of the proposed tracker to measure the local similarities between the template of the tracked target and the sampled candidates. Finally, in order to improve the robustness of the proposed tracker, we integrate a simple scheme to update the target template throughout the tracking process. Experimental results on several challenging image sequences illustrate that the proposed method performs better against several state-of-the-art trackers.",
keywords = "Motion analysis, motion model, observation model, soft cosine measure, soft similarity, visual tracking",
author = "Driss Moujahid and Omar Elharrouss and Hamid Tairi",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017 ; Conference date: 22-05-2017 Through 24-05-2017",
year = "2017",
month = oct,
day = "19",
doi = "10.1109/ATSIP.2017.8075521",
language = "English",
series = "Proceedings - 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Hamida, \{Ahmed Ben\} and Basel Solaiman and Slima, \{Ahmed Ben\} and \{El Hassouni\}, Mohammed and Mohammed Karim",
booktitle = "Proceedings - 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017",
}