Vehicle tracking process based on combination of SURF and color feature

Xiaofeng Lu; Lei Wang

doi:10.1117/12.2178870

4 March 2015 Vehicle tracking process based on combination of SURF and color feature

Xiaofeng Lu, Lei Wang

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 94430W (2015) https://doi.org/10.1117/12.2178870
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

Abstract

In this paper, we describe a novel method for visual vehicle tracking process based on the combination of speeded-up robust features (SURF) points and color feature. The whole tracking process is constructed in the framework of particle filter. To further improve the precision and stability of tracking, a dynamic update mechanism of target template is proposed to capture appearance changes. This mechanism includes two strategies: Adopting new feature points and discarding bad feature points. A novel distance kernel function method is adopted to allocate the weight of each particle, and to improve the stability of the tracking template. The experiments present that our algorithm can track the targets more robustly and adaptively than the traditional algorithms.

Citation Download Citation

Xiaofeng Lu and Lei Wang "Vehicle tracking process based on combination of SURF and color feature", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94430W (4 March 2015); https://doi.org/10.1117/12.2178870

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KEYWORDS

Particles

Detection and tracking algorithms

Particle filters

Optical tracking

Sensors

Visualization

Image processing

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