A hybrid gravitational search algorithm with swarm intelligence and deep convolutional feature for object tracking optimization

Large number of object trackers based on particle swarm optimization (PSO) and its variants have been published in the recent decade. However, the majority of algorithms does not perform well when evaluated against the online object tracking benchmark. In the analysis of the existing swarm intelligence based object trackers, pre-mature convergence, loss in particle information and inadequate feature are identified as the factors that hinder the performance of this class of trackers. In this regard, this paper proposes to use the hybrid gravitational search algorithm (HGSA) to increase the utilization of particle information and to facilitate thorough search inside the video frame before convergence. HGSA elegantly combines GSA's gravitational update component with the cognitive and social components of PSO using a novel weight function. The hybridized algorithm acquires the exploitation of past information and fast convergence property typical of PSO, while retaining the GSA capability in fully utilizing all current information. Moreover, the incorporation of deep convolutional feature is proposed to address the inadequacy of the weak hue, saturation and value (HSV) histogram feature. Experimental results using videos from the online tracking benchmark show that the proposed HGSA tracker with deep convolutional feature (DeepHGSA) has increased accuracy of ADSO, the best existing Swarm Intelligence based tracker, by 50.6% and robustness by 56.9% measured by area under curve. (C) 2018 Elsevier B.V. All rights reserved.