Detection of dynamic background due to swaying movements from motion features

Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: (1) that of swaying tree branches and (2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras.The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art - n the dynamic background literature

A nonlinear M-estimation approach to robust asynchronous multiuser detection in Non-gaussian noise

A nonlinear M-estimation approach is proposed to solve the multiuser detection problem in asynchronous code-division multiple-access (CDMA) systems where the ambient noise is impulsive and the delays are not known. We treat the unknown delays as nuisance parameters and the transmitted symbols as parameters of interest. We also analyze the asymptotic performance of the proposed estimator and propose suboptimal but computationally efficient procedures for solving the nonlinear optimization function. Simulation results show considerable improvements over the conventional approaches

An optimisation approach to robust estimation of mulitcomponent polynomial phase signals in non-Gaussian noise

In this paper, we address the problem of estimating the parameters of multicomponent polynomial phase signals in impulsive noise which arises in many practical situations. In the presence of this non-standard noise, existing techniques perform can poorly. We propose a nonlinear M-estimation approach to improve the existing techniques. The phase parameters are obtained by solving a nonlinear optimisation problem. A procedure is proposed to find the global minimum at low computational cost. Simulation examples show the proposed method performs better than existing method

Interpreting text and image relations in violent extremist discourse: A mixed methods approach for big data analytics

This article presents a mixed methods approach for analysing text and image relations in violent extremist discourse. The approach involves integrating multimodal discourse analysis with data mining and information visualisation, resulting in theoretically informed empirical techniques for automated analysis of text and image relations in large datasets. The approach is illustrated by a study which aims to analyse how violent extremist groups use language and images to legitimise their views, incite violence, and influence recruits in online propaganda materials, and how the images from these materials are re-used in different media platforms in ways that support and resist violent extremism. The approach developed in this article contributes to what promises to be one of the key areas of research in the coming decades: namely the interdisciplinary study of big (digital) datasets of human discourse, and the implications of this for terrorism analysis and research

A fast extension for sparse representation on robust face recognition

We extend a recent Sparse Representation-based Classification (SRC) algorithm for face recognition to work on 2D images directly, aiming to reduce the computational complexity whilst still maintaining performance. Our contributions include: (1) a new 2D extension of SRC algorithm; (2) an incremental computing procedure which can reduce the eigen decomposition expense of each 2D-SRC for sequential input data; and (3) extensive numerical studies to validate the proposed methods

Improved Impulse Noise Removal with Generalized Median Filter

The median filter is widely used for removing impulse noise in images due to its good denoising property whilst maintaining reasonably edge preservation. When the noise level is large, it can be further improved by combining with a detail-preserving regularization to ensure satisfactory edge recovery. We propose an improvement over a state-of-the-art impulse noise removal method which was demonstrated to cope well with very high impulsive noise levels. We introduce a novel generalized median filter, which is a new perspective based on latest advances in matrix decomposition and allows an explicit noise modelling. We provide comprehensive theoretical justifications for the proposed generalized median filter and demonstrate its effectiveness in recovering noisy images tempered with salt-and-pepper corruptions when combined with detail-preserving regularization over other relevant alternatives

On group-wise ℓp regularization: Theory and efficient algorithms

Following advances in compressed sensing and high-dimensional statistics, many pattern recognition methods have been developed with ℓ1 regularization, which promotes sparse solutions. In this work, we instead advocate the use of ℓp (2 ≥ p > 1) regularization in a group setting which provides a better trade-off between sparsity and algorithmic stability. We focus on the simplest case with squared loss, which is known as group bridge regression. On the theoretical side, we prove that group bridge regression is uniformly stable and thus generalizes, which is an important property of a learning method. On the computational side, we make group bridge regression more practically attractive by deriving provably convergent and computationally efficient optimization algorithms. We show that there are at least several values of p over (1,2) at which the iterative update is analytical, thus it is even suitable for large-scale settings. We demonstrate the clear advantage of group bridge regression with the proposed algorithms over other competitive alternatives on several datasets. As ℓp-regularization allows one to achieve flexibility in sparseness/denseness of the solution, we hope that the algorithms will be useful for future applications of this regularization

Analysis of multicomponent polynomial phase signals

While the theory of estimation of monocomponent polynomial phase signals is well established, the theoretical and methodical treatment of multicomponent polynomial phase signals (mc-PPSs) is limited. In this paper, we investigate several aspects of parameter estimation for mc-PPSs and derive the Crameacuter-Rao bound. We show the limits of existing techniques and then propose a nonlinear least squares (NLS) approach. We also motivate the use the Nelder-Mead simplex algorithm for minimizing the nonlinear cost function. The slight increase in computational complexity is a tradeoff for improved mean square error performance, which is evidenced by simulation results