A Context Space Model for Detecting Anomalous Behaviour in Video Surveillance

Having a good automatic anomalous human behaviour detection is one of the goals of smart surveillance systems’ domain of research. The automatic detection addresses several human factor issues underlying the existing surveillance systems. To create such a detection system, contextual information needs to be considered. This is because context is required in order to correctly understand human behaviour. Unfortunately, the use of contextual information is still limited in the automatic anomalous human behaviour detection approaches. This paper proposes a context space model which has two benefits: (a) It provides guidelines for the system designers to select information which can be used to describe context; (b)It enables a system to distinguish between different contexts. A comparative analysis is conducted between a context-based system which employs the proposed context space model and a system which is implemented based on one of the existing approaches. The comparison is applied on a scenario constructed using video clips from CAVIAR dataset. The results show that the context-based system outperforms the other system. This is because the context space model allows the system to considering knowledge learned from the relevant context only.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an OpenCL implementation of a PCNN feature generator. Its performance is compared across OpenCL kernels designed for CPU, GPU, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection.

Is bus overrepresented in Bluetooth MAC Scanner data? Is MAC-ID really unique?

One of the concerns about the use of Bluetooth MAC Scanner (BMS) data, especially from urban arterial, is the bias in the travel time estimates from multiple Bluetooth devices being transported by a vehicle. For instance, if a bus is transporting 20 passengers with Bluetooth equipped mobile phones, then the discovery of these mobile phones by BMS will be considered as 20 different vehicles, and the average travel time along the corridor estimated from the BMS data will be biased with the travel time from the bus. This paper integrates Bus Vehicle Identification system with BMS network to empirically evaluate such bias, if any. The paper also reports an interesting finding on the uniqueness of MAC IDs.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.

Higher order spectra based support vector machine for arrhythmia classification

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.

Heterogeneity of miR-10b expression in circulating tumor cells

Circulating tumor cells (CTCs) in the blood of cancer patients are recognized as important potential targets for future anticancer therapies. As mediators of metastatic spread, CTCs are also promising to be used as € liquid biopsyto aid clinical decision-making. Recent work has revealed potentially important genotypic and phenotypic heterogeneity within CTC populations, even within the same patient. MicroRNAs (miRNAs) are key regulators of gene expression and have emerged as potentially important diagnostic markers and targets for anti-cancer therapy. Here, we describe a robust in situ hybridization (ISH) protocol, incorporating the CellSearch ® CTC detection system, enabling clinical investigation of important miRNAs, such as miR-10b on a cell by cell basis. We also use this method to demonstrate heterogeneity of such as miR-10b on a cell-by-cell basis. We also use this method to demonstrate heterogeneity of miR-10b in individual CTCs from breast, prostate and colorectal cancer patients.

Infra-red pupil detection for use in a face recognition system

This paper presents a new method of eye localisation and face segmentation for use in a face recognition system. By using two near infrared light sources, we have shown that the face can be coarsely segmented, and the eyes can be accurately located, increasing the accuracy of the face localisation and improving the overall speed of the system. The system is able to locate both eyes within 25% of the eye-to-eye distance in over 96% of test cases.

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Detection of anomalies from user profiles generated from system logs

We describe research into the identification of anomalous events and event patterns as manifested in computer system logs. Prototype software has been developed with a capability that identifies anomalous events based on usage patterns or user profiles, and alerts administrators when such events are identified. To reduce the number of false positive alerts we have investigated the use of different user profile training techniques and introduce the use of abstractions to group together applications which are related. Our results suggest that the number of false alerts that are generated is significantly reduced when a growing time window is used for user profile training and when abstraction into groups of applications is used.

An Android application sandbox system for suspicious software detection

Smartphones are steadily gaining popularity, creating new application areas as their capabilities increase in terms of computational power, sensors and communication. Emerging new features of mobile devices give opportunity to new threats. Android is one of the newer operating systems targeting smartphones. While being based on a Linux kernel, Android has unique properties and specific limitations due to its mobile nature. This makes it harder to detect and react upon malware attacks if using conventional techniques. In this paper, we propose an Android Application Sandbox (AASandbox) which is able to perform both static and dynamic analysis on Android programs to automatically detect suspicious applications. Static analysis scans the software for malicious patterns without installing it. Dynamic analysis executes the application in a fully isolated environment, i.e. sandbox, which intervenes and logs low-level interactions with the system for further analysis. Both the sandbox and the detection algorithms can be deployed in the cloud, providing a fast and distributed detection of suspicious software in a mobile software store akin to Google's Android Market. Additionally, AASandbox might be used to improve the efficiency of classical anti-virus applications available for the Android operating system.

A new Salmonella typhimurium NM5004 strain expressing rat glutathione S-transferase 5-5 : use in detection of genotoxicity of dihaloalkanes using an SOS/umu test system

The Escherichia coli mu operon was subcloned into a pKK233-2 vector containing rat glutathione S-transferase (GST) 5-5 cDNA and the plasmid thus obtained was introduced into Salmonella typhimurium TA1535. The newly developed strain S.typhimurium NM5004, was found to have 52-fold greater GST activity than the original umu strain S.typhimurium TA1535/pSK1002. We compared sensitivities of these two tester strains, NM5004 and TA1535/ pSK1002, for induction of umuC gene expression with several dihaloalkanes which are activated or inactivated by GST 5-5 activity. The induction of umuC gene expression by these chemicals was monitored by measuring the cellular P-galactosidase activity produced by umuC'lacZ fusion gene in these two tester strains. Ethylene dibromide, 1-bromo-2-chloroethane, 1,2-dichloroethane, and methylene dichloride induced umuC gene expression more strongly in the NM5004 strain than the original strain, 4-Nitroquinoline 1-oxide and N-methyl-N'-nitro-N-nitrosoguanidine were found to induce umuC gene expression to similar extents in both strains. In the case of 1-nitropyrene and 2-nitrofluorene, however, NM5004 strain showed weaker umuC gene expression responses than the original TA1535/ pSK1002 strain, 1,2-Epoxy-3-(4'-nitrophenoxy)propane, a known substrate for GST 5-5, was found to inhibit umuC induction caused by 1-bromo-2-chloroethane. These results indicate that this new tester NM5004 strain expressing a mammalian GST theta class enzyme may be useful for studies of environmental chemicals proposed to be activated or inactivated by GST activity.

An efficient and robust system for multi-person event detection in real world indoor surveillance scenes

Due to the popularity of security cameras in public places, it is of interest to design an intelligent system that can efficiently detect events automatically. This paper proposes a novel algorithm for multi-person event detection. To ensure greater than real-time performance, features are extracted directly from compressed MPEG video. A novel histogram-based feature descriptor that captures the angles between extracted particle trajectories is proposed, which allows us to capture motion patterns of multi-person events in the video. To alleviate the need for fine-grained annotation, we propose the use of Labelled Latent Dirichlet Allocation, a “weakly supervised” method that allows the use of coarse temporal annotations which are much simpler to obtain. This novel system is able to run at approximately ten times real-time, while preserving state-of-theart detection performance for multi-person events on a 100-hour real-world surveillance dataset (TRECVid SED).