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	1. Kernel Correlation Filter Tracking based on Spatial Constraint
	LI Zhi-Yong,Chen Li
	Information Science and System Science 25 April 2018
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Correlation filter (CF) based trackers have become quite popular in video tracking because of their impressive performance and high frame rates. A large amount of recent research focuses on the improvement of training model of correlation filter to get a tracker with better discriminative power. However, this only helps the tracker to discriminate the target object from background within a small neighborhood, which is not suitable for fast motion scenes. In this paper, we propose a new detection model to dig out the potential of the correlation filter to deal with the challenge of fast motion. The model performs detection operations on multiple small search areas within a large one. Thus, our tracker can accurately localize the target object in a larger search area. In addition, we also added space constraints to boost the tracking performance of the model. The extensive experimental results demonstrate that the proposed tracker outperforms several state-of-the-art trackers on the challenging benchmark dataset with 51 sequences.
	TO cite this article:LI Zhi-Yong,Chen Li. Kernel Correlation Filter Tracking based on Spatial Constraint[OL].[25 April 2018] http://en.paper.edu.cn/en_releasepaper/content/4744779


	2. Discrete Particle Swarm Optimization for Terminal Assignment Problems
	WANG Jiahai,CAI Yiqiao
	Information Science and System Science 19 August 2010
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	Abstract:This paper presents a novel discrete particle swarm optimization (PSO) based on estimation of distribution (EDA), named DPSO-EDA, for terminal assignment problem (TEAP). EDAs sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from personal best solutions of all particles into the PSO, and therefore each particle has comprehensive learning and search ability. Simulation results on several problem instances show that the DPSO-EDA is better than previous methods.
	TO cite this article:WANG Jiahai,CAI Yiqiao. Discrete Particle Swarm Optimization for Terminal Assignment Problems[OL].[19 August 2010] http://en.paper.edu.cn/en_releasepaper/content/4382268


	3. A fictitious play approach for minimizing the total weighted tardiness in a job shop
	Gu Hanyu ,Tao Jiping ,Xi Yugeng
	Information Science and System Science 30 April 2010
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	Abstract:A noncooperative game model is established for the job shop scheduling problem of minimizing total weighted tardiness. Fictitious play is used to compute the Nash-equilibrium (NE). Then by exploiting the equivalence of the NE of the proposed game model and the optimal solutions of the Lagrangian dual problem and its linear programming dual problem, $\\alpha$-point idea is applied to construct feasible schedules. Furthermore a simple local search method is designed to further improve the schedule performance. Numerical results on classical benchmark instances demonstrate the effectiveness of the proposed algorithm.
	TO cite this article:Gu Hanyu ,Tao Jiping ,Xi Yugeng . A fictitious play approach for minimizing the total weighted tardiness in a job shop[OL].[30 April 2010] http://en.paper.edu.cn/en_releasepaper/content/42513

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