Local Similarity Number and its Application to Object Tracking

• Published in: International journal of advanced robotic systems Vol. 10; no. 3
• Main Authors: Tavakoli, Hamed Rezazadegan, Moin, M. Shahram, Heikkilä, Janne
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-03-2013; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Color; Histograms; Pixels; Representations; Salience; Similarity numbers; Texture; Tracking; Local Binary Patterns; Mean-shift Tracking; Saliency
• ISSN: 1729-8806; 1729-8814
• DOI: 10.5772/55337

In this paper, we present a tracking technique utilizing a simple saliency visual descriptor. Initially, we define a visual descriptor named local similarity pattern that mimics the famous texture operator local binary patterns. The key difference is that it assigns each pixel a code based on the similarity to the neighbouring pixels. Later, we simplify this descriptor to a local saliency operator which counts the number of similar pixels in a neighbourhood. We name this operator local similarity number (LSN).
We apply the local similarity number operator to measure the amount of saliency in a target patch and model the target. The proposed tracking algorithm uses a joint saliency-colour histogram to represent the target in a mean-shift tracking framework. We will show that the proposed saliency-colour target representation outperforms texture-colour where texture modelled by local binary patterns and colour target representation techniques are used.