Abstract
This paper proposes a new approach to object tracking using the Hybrid Gravitational Search Algorithm (HGSA). HGSA introduces the Gravitational Search Algorithm (GSA) to the field of object tracking by incorporating Particle Swarm Optimization (PSO) using a novel weight function that elegantly combines GSA’s gravitational update component with the cognitive and social components of PSO. The hybridized algorithm acquires PSO’s exploitation of past information and fast convergence property while retaining GSA’s capability in fully utilizing all current information. The proposed framework is compared against standard natural phenomena based algorithms and Particle Filter. Experiment results show that HGSA largely reduces convergence to local optimum and significantly out-performed the standard PSO algorithm, the standard GSA and Particle Filter in terms of tracking accuracy and stability under occlusion and non-linear movement in a large search space.
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Yeung, H.W.F., Liu, G., Chung, Y.Y., Liu, E., Yeh, WC. (2016). Hybrid Gravitational Search Algorithm with Swarm Intelligence for Object Tracking. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_23
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DOI: https://doi.org/10.1007/978-3-319-46687-3_23
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