Most of the existing tracking methods based on CNN(convolutional neural networks) are too slow for real-time application despite the excellent tracking precision compared with the traditional ones. In this paper, a fast dynamic visual tracking algorithm combining CNN based MDNet(Multi-Domain Network) and RoIAlign was developed. The major problem of MDNet also lies in the time efficiency. Considering the computational complexity of MDNet is mainly caused by the large amount of convolution operations and fine-tuning of the network during tracking, a RoIPool layer which could conduct the convolution over the whole image instead of each RoI is added to accelerate the convolution and a new strategy of fine-tuning the fully-connected layers is used to accelerate the update. With RoIPool employed, the computation speed has been increased but the tracking precision has dropped simultaneously. RoIPool could lose some positioning precision because it can not handle locations represented by floating numbers. So RoIAlign, instead of RoIPool, which can process floating numbers of locations by bilinear interpolation has been added to the network. The results show the target localization precision has been improved and it hardly increases the computational cost. These strategies can accelerate the processing and make it 7 $\times$ faster than MDNet with very low impact on precision and it can run at around 7 fps. The proposed algorithm has been evaluated on two benchmarks: OTB100 and VOT2016, on which high precision and speed have been obtained. The influence of the network structure and training data are also discussed with experiments. The source code is publicly available on github: https://github.com/ZhiyanCui/fdn-release

In this paper, energy efficiency (EE)-oriented partial spectrum reuse (PSR) scheme for heterogeneous cellular networks (HCN) with wired backhaul is studied with the aid of stochastic geometry theory. We first derive the total expected delay for a typical user by taking into account retransmissions over the wireless link, as well as the backhaul delay incurred from wired backhaul. Furthermore, we minimize the whole delay experienced by the users from the perspective of small cell density and the minimization of the total energy cost then can be addressed relying on the optimal small cell density and PSR factor. The main takeaway is that if we account for the backhaul, it can significantly reverse the relationship between the total network energy cost and the delay experienced by the users with varying signal to interference ratio (SIR) threshold. Theoretical analysis results indicate that, unlike previous known conclusions, sacrificing delay cannot always be traded for energy saving, and the feasible conditions under which the optimal energy-delay tradeoff exists are also obtained. Moreover, the optimal pair of PSR factor and SIR threshold to achieve the optimal energy-delay tradeoff is also derived. Both the analytical and simulation results show that significant energy saving can be achieved through tolerable sacrifice of delay performance with careful designs according to our analysis.