Mining anomalous events against frequent sequences in surveillance videos from commercial environments

In the UK alone there are currently over 4.2 million operational CCTV cameras, that is virtually one camera for every 14th person, and this figure is increasing at a fast rate throughout the world (especially after the tragic events of 9/11 and 7/7) (Norris, McCahill, & Wood, 2004). Security concerns are not the only factor driving the rapid growth of CCTV cameras. Another important reason is the access of hidden knowledge extracted from CCTV footage to be used for effective business decision making, such as store designing, customer services, product marketing, reducing store shrinkage, etc.Events occurring in observed scenes are one of the most important semantic entities that can be extracted from videos (Anwar & Naftel, 2008). Most of the work presented in the past is based upon finding frequent event patterns or deals with discovering already known abnormal events. In contrast, in this paper we present a framework to discover unknown anomalous events associated with a frequent sequence of events (A_EASP); that is to discover events, which are unlikely to follow a frequent sequence of events. This information can be very useful for discovering unknown abnormal events and can provide early actionable intelligence to redeploy resources to specific areas of view (such as PTZ camera or attention of a CCTV user). Discovery of anomalous events against a sequential pattern can also provide business intelligence for store management in the retail sector. The proposed event mining framework is an extension to our previous research work presented in Anwar et al. (2010) and also takes the temporal aspect of anomalous events against frequent sequence of events into consideration, that is to discover anomalous events which are true for a specific time interval only and might not be an anomalous events against frequent sequence of events over a whole time spectrum and vice versa. To confront the memory expensive process of searching all the instances of multiple sequential patterns in each data sequence an efficient dynamic sequential pattern search mechanism is introduced. Different experiments are conducted to evaluate the proposed anomalous events against frequent sequence of events mining algorithm’s accuracy and performance.     

GOP ARIMA: Modeling the nonstationarity of VBR processes

In this work, we develop a stochastic model, GOP ARIMA (autoregressive integrated moving average for a group of pictures) for VBR processes with a regular GOP pattern. It explicitly incorporates the deterministic time-dependent behavior of frame-level VBR traffic. The GOP ARIMA model elaborately represents the inter- and intra-GOP sample autocorrelation structures and provides a physical explanation of observed stochastic characteristics of the empirical VBR process. We explain stochastic characteristics of the empirical VBR process, e.g., slowly decaying sample autocorrelations and strong correlations at the lags, based on the aspect of nonstationarity of the underlying process. The GOP ARIMA model generates synthetic traffic, which has the same multiplicative periodic sample autocorrelation structure as well as slowly decaying autocorrelations of the empirical VBR process. The simulation results show that the GOP ARIMA process very well captures the behavior of the empirical process in various respects: packet loss, packet delay, and frame corruption. Our work makes a contribution not only toward providing a theoretical explanation of the observed characteristics of the empirical VBR process but also toward the development of an efficient method for generating a more realistic synthetic sequence for various engineering purposes and for predicting future bandwidth requirements.     

统计过程控制(spc)及MATLAB实现

统计过程控制是利用统计技术来控制生产过程。本文对HATLAB实现SPCX—R图作了介绍。并基于MATLAB对控制图中各种失控情况的判定方面进行了探讨。针对统计过程中的‘周期性’的判定问题,提出了利用快速傅里叶变换,根据功率谱密度来判断的方法。     

新型精梳机40 cm～50 cm精梳条波形分析

探讨消除新型精梳机棉条上波长为40 cm～50 cm渡的解决办法.分析了该波的特点和形成原因,认为新型精梳机中某些机构的往复运动和交变载荷是造成该波的主要原因,该波应是机械波而不是搭接波.指出:在当前使用的新型精梳机上,这种机械波是消除不掉的,只能在一定程度上减小其峰值,而在新机设计中通过采用切断交变载荷对牵伸、圈条等机构的传动影响等设计,可消除该机械波.     

Quasi-Chun- Ching Shih＇s Fractional Fourier Transform with Periodicity of 2,3 and M

Based on Chun-Ching Shih‘s idea, the basic transform was substituted and the quasi-Chun-Ching Shih‘s fractional Fourier transform with periodicity of 2, 3 and M was deduced. The two former transforms and the Chun-Ching Shih‘s fractional Fourier transform were only the particular cases of quasi-Chun-Ching Shih‘s fractional Fourier transform with periodicity of M.     

一种语音信号非周期性、周期性及基频检测的改进方法

APP方法可以准确检测语音信号中的非周期性、周期性和基频,是近年提出的一种先进检测新方法,对于语音基础研究和语音技术应用研究有重要作用。APP方法的最大优点是可以同时检测语音信号的基频周期、周期成分和非周期成分的能量比例,而最大缺点是计算代价巨大,运行时间为110倍实时,成为实际应用的最大障碍。该文在深入剖析APP方法的基础上,从原理架构和技术实现两个方面消除不合理的冗余处理,提出新的改进途径,发展成为改进的APP方法,即MAPP方法。MAPP方法不但加强了APP方法处理机制的合理性基础,改善基频检测的准确性和鲁棒性,而且提高计算效率约1个数量级,在CPU时钟频率为1.70GHz和内存为512MB的Pentium 计算机上的运行时间加快到12.3倍实时。     

话务量预测模型的建立与仿真研究

研究话务量预测精度问题,由于话务量受到节假日、周末等影响,记录数据浮动性大,存在周期性和时变性等特点。传统预测方法对工作日和节假日话务量采用同一个模型预测,导致预测结果不稳定,时高时低。为了提高话务量预测精度,针对话务量变化特点,提出一种支持向量机的话务量预测模型。利用话务量数据分正常工作日和周末两大类,再分别用支持向量机建立预测模型,采用模型对相应时间段话务量进行预测。实验结果表明,支持向量机模型提高了话务量预测精度,减少了预测误差,更准确刻画了话务量变化规律,为话务量网络管理提供了一种新的预测工具。     

JPEG quantization step estimation with coefficient histogram and spectrum analyses

This paper proposes a new method for estimating quantization steps (QSs) from an image that has been previously JPEG-compressed and stored in a lossless format. In this method, DCT coefficients of each frequency band of JPEG-compressed image are aggregated in the QS and its multiples. The entire estimation process can be grouped into two categories: alternating and direct current bands. Considering that DCT coefficients under different QSs show different periodicity, QS estimation for each band is then further divided into three steps, which involve identifying whether the QS is one, two, or another value. For each step, the periodicity of DCT coefficients can be well exploited with the analyses of the DCT-coefficient histogram and its corresponding frequency magnitude spectrum. Experimental results demonstrate the efficacy of the proposed method and the superiority in QS estimation for previously JPEG-compressed images, especially in the case that the actual QSs are higher than two.     

邯郸平原区降水丰枯多时间尺度特性

降水丰枯特性如何是分析降水过程的重要内容。以邯郸平原水资源分区为基本研究单元,运用小波分析法,就各分区降水丰枯特性进行了多时间尺度分析,结果表明:邯郸市各平原分区降水过程都存在5a、9a左右为变化的主要周期,除了滏西平原外,其他平原区降水均还存在2a和13a的主周期变化,且漳卫河和黑龙港两平原区降水21a左右变化的主周期也较为明显;各分区降水在5a和9a主周期尺度上,均有明显的丰枯交替过程;不同分区降水在不同周期尺度的丰枯响应上存在一定的异质性并表现出不同的丰枯特性和演变趋势。上述成果可为区域旱涝预警和水资源管理提供可靠依据。     

基于改进归一化距离匹配函数的纹理周期自动提取方法

针对规则纹理和近似规则纹理,提出基于改进归一化距离匹配函数(INDMF)的纹理周期自动提取方法.该方法首先利用灰度共生矩阵的差异性作为纹理特征,去除改进归一化匹配函数的边缘,有效优化函数峰值间的稳定性.然后使用自适应峰值寻找算法去除噪声干扰,获得初始峰值序列并进行周期提取.最后使用众数计算最优周期.分别对Brodatz纹理和PSU周期纹理进行提取实验,结果显示文中方法运行效率较高,能有效提取自然纹理的结构周期.与累加DMF向前差分法相比,文中方法具有更好的抗噪声和抗畸变能力.