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This work applies the Gaussian Mixture Probability Hypothesis Density (GMPHD) Filter to multi-object tracking in video data. In order to take advantage of additional visual information, Kernelized Correlation Filters (KCF) are evaluated as a possible extension of the GMPHD tracking-by-detection scheme to enhance its performance. The baseline GMPHD filter and its extension are evaluated on the UA-DETRAC...
Recently, kernelized correlation Filter-based trackers have aroused the interest of many researchers and achieved good results in the field of tracking. However, the current tracking model based on kernelized correlation filters can not deal with the changes of the target appearance and scale effectively. Therefore, in this paper, we intend to solve these two problems and improve the robustness of...
We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the tracking target. Our contributions are manifold, and are summarised as follows: (i) Introducing the Attentional...
Correlation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small...
In this work we derive a novel clustering scheme for hyperspectral pixels according to the material they sense. We utilize statistical correlations that pixels sensing the same material exhibit. Specifically, kernel learning is combined with a norm-one regularized canonical correlations framework that can perform data clustering on nonlinearly dependent data. To tackle the derived minimization formulation...
At present, it is a great challenge that solving high-dimension and text sparsity problems in short text classification. To resolve these problems, this paper proposes a method which takes the correlation between lexical items and tags before completing Latent Dirichlet Allocation(LDA) topic model. Meanwhile, this paper adjusts parameters of Support Vector Machine(SVM) to find the optimal values by...
Based on the traffic accident data of Beijing China in 2012, we combined with a variety of municipal administration data, used the geographically weighted regression (GWR) method to study spatial non-stationarity and heterogeneity of the traffic accidents, and analyzed the causes of space regional in traffic accident black spots. Experimental results demonstrated that: (1) The GWR model detects change...
In this paper, we discuss a function reconstruction problem by kernel regressors in which the autocorrelation of the unknown true function is given a priori. In general, a reconstructed function in the kernel regression problem, using a certain reproducing kernel Hilbert space, is represented by a linear combination of the corresponding kernel specified by each input point. We introduce a framework...
We present our recent work on the Weyl-Heisenberg ensemble and its statistical properties [4]. The WH ensemble is a class of determinantal point processes associated with the Schrödinger representation of the Heisenberg group. As a special example, WH ensembles include a multi-layer extension of the Ginibre ensemble modeling the distribution of electrons in higher Landau levels. We describe the hyperuniformity...
Cross-media retrieval, which uses a text query to search for images and vice-versa, has attracted a wide attention in recent years. The mostly existing cross-media retrieval methods aim at finding a common subspace and maximizing different modalities correlations. But these approaches do not directly capture the underlying semantic information of different modalities. This paper proposes a novel cross-media...
In this paper, we focus on promoting multi-label learning task with ensemble learning. Compared to traditional single algorithm methods, it has been recognized that ensemble methods could achieve much better performance than each constituent learned model, especially under the conditional independence of different classifiers. Existing multi-label ensemble algorithms mainly focus on creating diverse...
Sparse dictionary selection (SDS) has demonstrated to be an effective solution for keyframe based video summarization (VS), which generally assumes a linear relation among similar video frames. However, such a linear assumption is not always true for videos. In this paper, the nonlinearity among frames is taken into consideration and a nonlinear SDS model is formulated for VS, in which the nonlinearity...
Accurate and robust state estimation is a fundamental problem in signal processing. Particle filter is an effective tool to solve the filtering problem in nonlinear stochastic dynamic systems. However, when the system is mean-field dependent and the data is high-dimensional in spatial and temporal domain, the estimator may become inaccurate or even diverge. In this paper, we propose a Correlative...
The Kernelized Correlation Filters (KCF) tracker has caused the extensive concern in recent years because of the high efficiency. Numberous improvements have been made later. However, due to the large displacement motion between the consecutive image frames, these methods cannot track object well. To better cope the problem, we proposed a new simulated annealed KCF (SAKCF) tracker. Take advantage...
This paper proposes a new intrinsic image decomposition method that decomposes a single RGB-D image into reflectance and shading components. We observe and verify that, a shading image mainly contains smooth regions separated by curves, and its gradient distribution is sparse. We therefore use ℓ1-norm to model the direct irradiance component — the main sub-component extracted from shading component...
Visual tracking is intrinsically a temporal problem. Discriminative Correlation Filters (DCF) have demonstrated excellent performance for high-speed generic visual object tracking. Built upon their seminal work, there has been a plethora of recent improvements relying on convolutional neural network (CNN) pretrained on ImageNet as a feature extractor for visual tracking. However, most of their works...
Due to the simplicity of its implementation and the impressive performance, Extreme Learning Machine (ELM) has been widely used in applications of machine learning. However, there are two potential problems in ELM: 1) lack of an efficient method for minimizing error; 2) consideration of little inherent structural information about correlations among output components. To overcome those problems, this...
Signal strength difference (SSD) is widely utilized as the feature for Wi-Fi fingerprint localization to tackle the heterogeneity between training device and target device, but the correlation between SSDs is largely ignored. In this paper, a novel scheme named LC-KDE is proposed. It utilizes local Fisher discriminant analysis (LFDA) to transform the original SSDs into weakly correlated features,...
Today Unmanned Aerial Systems (UAS) are widely used for many applications that involve advanced payload as is found to be the case for mounted remote sensing apparatus. Remote sensing from UAS platforms is now common and the use of light and smart multi/hyper-spectral cameras has opened the field to novel applications. These sensors can operate in cloudy conditions ensuring ultra high resolution images...
As seen in many studies the relationship of object oriented matrices of the software and the calculated maintenance effort metric is very complicated, complex and nonlinear in nature. So with this kind of behavior, we can have got a research area where we can work upon to minimize the maintenance effort which can be used to develop and deploy models and systems for the forecasting of software maintenance...
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