Correlation filter-based trackers have gained more and more attention because of their great performance and relative high tracking speed. However, this kind of trackers may suffer model drifting due to learning limited background information during filter training. This may lead to tracking failures in some complex scenes, such as background clutter, deformation, illumination variation and so on. In this paper, we propose an adaptive and complementary correlation filter with dynamic contextual constraints. First, we introduce contextual information around the target as a dynamic constrained term to alleviate model drifting in complex scenes, the optimal function of which can be solved by iterative method. Then, we integrate a color histogram-based tracker to compensate the inaccurate tracking of correlation filtering. In addition, we present metrics to combine the two complementary trackers with adaptive fusion coefficients. Finally, extensive experiments on OTB2013, OTB2015, VOT2016 and UAV123 benchmark datasets demonstrate that our tracker can improve the performance of our baseline and can perform favorably against some stateof-the-art trackers.

Recent discriminative trackers especially based on Correlation Filters (CFs) have shown dominant performance for visual tracking. This kind of trackers benefit from multi-resolution deep features a lot, taking the expressive power of deep Convolutional Neural Networks (CNN). However, distractors in complex scenarios, such as similar targets, occlusion, and deformation, lead to model drift. Meanwhile, learning deep features results in feature redundancy that the increasing number of learning parameters introduces the risk of over-fitting. In this paper, we propose a discriminative CFs based visual tracking method, called dimension adaption correlation filters (DACF). First, the framework adopts the multichannel deep CNN features to obtain a discriminative sample appearance model, resisting the background clutters. Moreover, a dimension adaption operation is introduced to reduce relatively irrelevant parameters as possible, which tackles the issue of over-fitting and promotes the module effectively adapting to different tracking scenes. Furthermore, the DACF formulation optimization can be efficiently performed on the basis of implementing the alternating direction method of multipliers (ADMM). Extensive evaluations are conducted on benchmarks, including OTB2013, OTB2015, VOT2016, and UAV123. The experiments results show that our tracker gains remarkable performance. Especially, DACF obtains an AUC score of 0.698 on OTB2015.INDEX TERMS Correlation filters, multi-channel feature learning, object tracking.

In order to solve the problem of spectrum fragment in elastic optical networks, a spectrum reconstruction algorithm based on resource partition allocation is proposed in this paper. Services are classified according to the different frequency of the services that occupy different number of frequency slots in the networks. Region division of spectrum resources is made according to the type of services and services are allocated in the corresponding spectrum partitions. When defragmenting in the networks, we reconstruct the spectrum for different regions with comprehensively considering the factors in time domain and frequency domain, integrating current resources and preventing the generation of spectrum fragment in the future. Simulation results show that the algorithm can reduce the degree of network fragmentation and reduce traffic blocking.

Based on the panel smooth transition regression (PSTR) model, this paper empirically analyzes the relationship between Chinese local government’s bond financing and economic growth, with the quarterly panel data of bonds issued by local governments and their investment and financing platform companies in the open market from 2008 to 2018 as samples. The research shows that there is a gradual non-linear relationship between local government bond market financing and economic growth in China. With the increase of the scale of local government bond market financing in China, the effect of bond market financing on economic growth will gradually decline and have a negative effect. This result means that for developing countries like China, it is not advisable to rely solely on government investment to drive economic growth.