Visual object tracking based on adaptive multi-feature fusion in complex scenarios

Hengjun Wang

doi:10.1117/12.2540112

14 August 2019 Visual object tracking based on adaptive multi-feature fusion in complex scenarios

Hengjun Wang

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111792V (2019) https://doi.org/10.1117/12.2540112
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

The major problems of object tracking by the traditional particle filter include low robustness and high computational load. To solve this problem, an object tracking method based on multi-features fusion and the MeanShift is proposed. The method represent the candidate objects with color and edge features, which efficiently avoids the unstable problems when the single color feature is applied in complex scenarios. The MeanShift is used in the particle-sampling steps, and the number of optimal particles is online determined by measuring the difference among the positions of the particles which not only improves the accuracy of the particle but also keeps the diversity. Experimental results show the effectiveness on high robustness and computational efficiency in complex scenarios.

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Hengjun Wang "Visual object tracking based on adaptive multi-feature fusion in complex scenarios", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111792V (14 August 2019); https://doi.org/10.1117/12.2540112

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KEYWORDS

Particles

Detection and tracking algorithms

Particle filters

Video

Optical tracking

Visualization

RGB color model

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