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Single object tracking using particle filter framework and saliency-based weighted color histogram

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Abstract

Despite many years of research, object tracking remains a challenging problem, not only because of the variety of object appearances, but also because of the complexity of surrounding environments. In this research, we present an algorithm for single object tracking using a particle filter framework and color histograms. Particle filters are iterative algorithms that perform predictions in each iteration using particles, which are samples drawn from a statistical distribution. Color histograms are embedded in these particles, and the distances between histograms are used to measure likelihood between targets and observations. One downside of color histograms is that they ignore spatial information, which may produce tracking failure when objects appear that are similar in color. To overcome this disadvantage, we propose a saliency-based weighting scheme for histogram calculation. Given an image region, first its saliency map is generated. Next, its histogram is calculated based on the generated saliency map. Pixels located in salient regions have higher weights than those in others, which helps preserve the spatial information. Experimental results showed the efficiency of the proposed appearance model in object tracking under various conditions.

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Funding

This study was funded by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01057518).

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Authors and Affiliations

  1. Department of Electrical, Electronic, and Control Engineering, Hankyong National University, 327 Jungang-ro, Anseong-si, Gyeonggi-do, Republic of Korea

    Mai Thanh Nhat Truong, Myeongsuk Pak & Sanghoon Kim

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  1. Mai Thanh Nhat Truong
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  2. Myeongsuk Pak
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  3. Sanghoon Kim
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Correspondence to Sanghoon Kim.

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Truong, M.T.N., Pak, M. & Kim, S. Single object tracking using particle filter framework and saliency-based weighted color histogram. Multimed Tools Appl 77, 30067–30088 (2018). https://doi.org/10.1007/s11042-018-6180-5

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  • DOI: https://doi.org/10.1007/s11042-018-6180-5

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