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With the promulgation and implementation of a large number of renewable energy policies in China, the rapid development of renewable energy is accompanied by the prominent phenomenon of renewable energy abandoning wind and light. Energy storage technology is the key technology to promote the consumption of renewable energy. The government can promote the energy storage technology through the incentive policy of energy storage industry. Firstly, content analysis method is used to analyze China's energy storage policy, and five incentive policies for promoting energy storage technology are obtained. Secondly, built a game model of energy storage technology promotion based on the evolutionary game theory. Finally, use MATLAB software for numerical simulation. Numerical simulation results show that: (a) When the local government chooses to promote less, energy enterprises will eventually adopt the nonconfigure strategy; (b) when the local government chooses to promote more vigorously, energy enterprises will finally adopt the configure strategy; (c) increase in the total electricity sold by energy enterprises with energy storage devices, the sales price of energy stored per unit, the compensation price of energy stored per unit, tax relief standards, and incentive costs of local governments can promote energy enterprises to choose configure strategies; and (d) the reduction of unit energy storage cost, configure cost, and comprehensive tax rate can promote energy enterprises to choose configure strategy.

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Correlation filters have achieved appealing performance with high speed in recent years. The advantage of correlation filter-based tracking methods is mainly attributed to powerful features and effective online filter learning. However, the periodic assumption of the training data would introduce unwanted boundary effects, which severely degrade the discrimination power of the correlation filter. In this paper, we construct the spatial reliable map with deep features from Convolutional Neural Network, then the map is used to adjust the filter support to the part of the object suitable for tracking. In order to further improve the long-term tracking ability, we introduce temporal regularization to DCF training, which can deal with occlusion and deformation situations. The experimental results show that the proposed algorithm achieves high tracking success rate and accuracy. INDEX TERMS Visual tracking, correlation filter, convolutional neural network, spatial constraint, temporal regularization.