Robust Object Tracking Based on Self-adaptive Search Area

Discriminative correlation filter (DCF) based trackers have recently achieved excellent performance with great computational efficiency. However, DCF based trackers suffer boundary effects, which result in the unstable performance in challenging situations exhibiting fast motion. In this paper, we propose a novel method to mitigate this side-effect in DCF based trackers. We change the search area according to the prediction of target motion. When the object moves fast, broad search area could alleviate boundary effects and reserve the probability of locating object. When the object moves slowly, narrow search area could prevent effect of useless background information and improve computational efficiency to attain real-time performance. This strategy can impressively soothe boundary effects in situations exhibiting fast motion and motion blur, and it can be used in almost all DCF based trackers. The experiments on OTB benchmark show that the proposed framework improves the performance compared with the baseline trackers.Comment: 10 pages, 4 figures, 3 tables, SPIE 10th International Symposium on Multispectral Image Processing and Pattern Recognitio

The (2+1) Dirac Equations with δ\delta Potential

In this Letter the bound states of (2+1) Dirac equation with the cylindrically symmetric $\delta (r-r_{0})$-potential are discussed. It is surprisingly found that the relation between the radial functions at two sides of $r_{0}$ can be established by an SO(2) transformation. We obtain a transcendental equation for calculating the energy of the bound state from the matching condition in the configuration space. The condition for existence of bound states is determined by the Sturm-Liouville theorem.Comment: Latex 11 pages accepted by Found. Phys. Let

Exact Solutions to the Schr\"{o}dinger Equation for the Inverse-Power Potential in Two Dimensions

Utilizing an ${\it ansatz}$ for the eigenfunctions, we arrive at an exact closed form solution to the Schr\"{o}dinger equation with the inverse-power potential, $V(r)=ar^{-4}+br^{-3}+cr^{-2}+dr^{-1}$ in two dimensions, where the parameters of the potential $a, b, c, d$ satisfy a constraint.Comment: Latex file 9 pages and submit to Euro. Phys. J.

Exact Solutions to the Schr\"{o}dinger Equation for the potential V(r)=ar2+br−4+cr−6V(r)=a r^2+b r^{-4}+c r^{-6} in 2D

Making use of an ${\it ansatz}$ for the eigenfunctions, we obtain an exact closed form solution to the non-relativistic Schr\"{o}dinger equation with the anharmonic potential, $V(r)=a r^2+b r^{-4}+c r^{-6}$ in two dimensions, where the parameters of the potential $a, b, c$ satisfy some constraints.Comment: Latex file, pages 9 and 2 eps figures, accepted by J. Phys.

Long-range Effects on the Pyroelectric Coefficient of Ferroelectric Superlattice

Long-range effects on the pyroelectric coefficient of a ferroelectric superlattice consisting of two different ferroelectric materials are investigated based on the Transverse Ising Model. The effects of the interfacial coupling and the thickness of one period on the pyroelectric coefficient of the ferroelectric superlattice are studied by taking into account the long-range interaction. It is found that with the increase of the strength of the long-range interaction, the pyroelectric coefficient decreases when the temperature is lower than the phase transition temperature; the number of the pyroelectric peaks decreases gradually and the phase transition temperature increases. It is also found that with the decrease of the interfacial coupling and the thickness of one period, the phase transition temperature and the number of the pyroelectric peaks decrease.Comment: 19 pages, 7 figure

Banzhaf Random Forests

Random forests are a type of ensemble method which makes predictions by combining the results of several independent trees. However, the theory of random forests has long been outpaced by their application. In this paper, we propose a novel random forests algorithm based on cooperative game theory. Banzhaf power index is employed to evaluate the power of each feature by traversing possible feature coalitions. Unlike the previously used information gain rate of information theory, which simply chooses the most informative feature, the Banzhaf power index can be considered as a metric of the importance of each feature on the dependency among a group of features. More importantly, we have proved the consistency of the proposed algorithm, named Banzhaf random forests (BRF). This theoretical analysis takes a step towards narrowing the gap between the theory and practice of random forests for classification problems. Experiments on several UCI benchmark data sets show that BRF is competitive with state-of-the-art classifiers and dramatically outperforms previous consistent random forests. Particularly, it is much more efficient than previous consistent random forests.Comment: arXiv admin note: text overlap with arXiv:1302.4853 by other author

A PCA-Based Convolutional Network

In this paper, we propose a novel unsupervised deep learning model, called PCA-based Convolutional Network (PCN). The architecture of PCN is composed of several feature extraction stages and a nonlinear output stage. Particularly, each feature extraction stage includes two layers: a convolutional layer and a feature pooling layer. In the convolutional layer, the filter banks are simply learned by PCA. In the nonlinear output stage, binary hashing is applied. For the higher convolutional layers, the filter banks are learned from the feature maps that were obtained in the previous stage. To test PCN, we conducted extensive experiments on some challenging tasks, including handwritten digits recognition, face recognition and texture classification. The results show that PCN performs competitive with or even better than state-of-the-art deep learning models. More importantly, since there is no back propagation for supervised finetuning, PCN is much more efficient than existing deep networks.Comment: 8 pages,5 figure

Research on fuzzy PID Shared control method of small brain-controlled uav

Brain-controlled unmanned aerial vehicle (uav) is a uav that can analyze human brain electrical signals through BCI to obtain flight commands. The research of brain-controlled uav can promote the integration of brain-computer and has a broad application prospect. At present, BCI still has some problems, such as limited recognition accuracy, limited recognition time and small number of recognition commands in the acquisition of control commands by analyzing eeg signals. Therefore, the control performance of the quadrotor which is controlled only by brain is not ideal. Based on the concept of Shared control, this paper designs an assistant controller using fuzzy PID control, and realizes the cooperative control between automatic control and brain control. By evaluating the current flight status and setting the switching rate, the switching mechanism of automatic control and brain control can be decided to improve the system control performance. Finally, a rectangular trajectory tracking control experiment of the same height is designed for small quadrotor to verify the algorithm

Shared control schematic for brain controlled vehicle based on fuzzy logic

Brain controlled vehicle refers to the vehicle that obtains control commands by analyzing the driver's EEG through Brain-Computer Interface (BCI). The research of brain controlled vehicles can not only promote the integration of brain machines, but also expand the range of activities and living ability of the disabled or some people with limited physical activity, so the research of brain controlled vehicles is of great significance and has broad application prospects. At present, BCI has some problems such as limited recognition accuracy, long recognition time and limited number of recognition commands in the process of analyzing EEG signals to obtain control commands. If only use the driver's EEG signals to control the vehicle, the control performance is not ideal. Based on the concept of Shared control, this paper uses the fuzzy control (FC) to design an auxiliary controller to realize the cooperative control of automatic control and brain control. Designing a Shared controller which evaluates the current vehicle status and decides the switching mechanism between automatic control and brain control to improve the system control performance. Finally, based on the joint simulation platform of Carsim and MATLAB, with the simulated brain control signals, the designed experiment verifies that the control performance of the brain control vehicle can be improved by adding the auxiliary controller

Mildly relativistic X-ray transient 080109 and SN2008D: Towards a continuum from energetic GRB/XRF to ordinary Ibc SN

We analyze the hitherto available space-based X-ray data as well as ground-based optical data of the X-ray transient 080109/SN2008D. From the data we suggest that (i) The initial transient (\lesssim 800 sec) is attributed to the reverse shock emission of a mildly relativistic (\Gamma \sim a few) outflow stalled by the dense stellar wind. (ii) The subsequent X-ray afterglow (\lesssim 2\times 10^4 sec) can be ascribed to the forward shock emission of the outflow, with a kinetic energy \sim 10^{46} erg, when sweeping up the stellar wind medium. (iii) The late X-ray flattening (\gtrsim 2\times 10^4$ sec) is powered by the fastest non-decelerated component of SN2008D's ejecta. (iv) The local event rate of X-ray transient has a lower limit of \sim 1.6\times 10^4 yr^{-1} Gpc^{-3}, indicating a vast majority of X-ray transients have a wide opening angle of \gtrsim 100 degree. The off-axis viewing model is less likely. (v) Transient 080109/SN2008D may lead to a continuum from GRB-SN to under-luminous GRB-/XRF-SN to X-ray transient-SN and to ordinary Ibc SN (if not every Ibc SN has a relativistic jet), as shown in Figure 2 of this Letter.Comment: 4 pages, 2 figure