Video analytics for security systems

This study has been conducted to develop robust event detection and object tracking algorithms that can be implemented in real time video surveillance applications. The aim of the research has been to produce an automated video surveillance system that is able to detect and report potential security risks with minimum human intervention. Since the algorithms are designed to be implemented in real-life scenarios, they must be able to cope with strong illumination changes and occlusions. The thesis is divided into two major sections. The first section deals with event detection and edge based tracking while the second section describes colour measurement methods developed to track objects in crowded environments. The event detection methods presented in the thesis mainly focus on detection and tracking of objects that become stationary in the scene. Objects such as baggage left in public places or vehicles parked illegally can cause a serious security threat. A new pixel based classification technique has been developed to detect objects of this type in cluttered scenes. Once detected, edge based object descriptors are obtained and stored as templates for tracking purposes. The consistency of these descriptors is examined using an adaptive edge orientation based technique. Objects are tracked and alarm events are generated if the objects are found to be stationary in the scene after a certain period of time. To evaluate the full capabilities of the pixel based classification and adaptive edge orientation based tracking methods, the model is tested using several hours of real-life video surveillance scenarios recorded at different locations and time of day from our own and publically available databases (i-LIDS, PETS, MIT, ViSOR). The performance results demonstrate that the combination of pixel based classification and adaptive edge orientation based tracking gave over 95% success rate. The results obtained also yield better detection and tracking results when compared with the other available state of the art methods. In the second part of the thesis, colour based techniques are used to track objects in crowded video sequences in circumstances of severe occlusion. A novel Adaptive Sample Count Particle Filter (ASCPF) technique is presented that improves the performance of the standard Sample Importance Resampling Particle Filter by up to 80% in terms of computational cost. An appropriate particle range is obtained for each object and the concept of adaptive samples is introduced to keep the computational cost down. The objective is to keep the number of particles to a minimum and only to increase them up to the maximum, as and when required. Variable standard deviation values for state vector elements have been exploited to cope with heavy occlusion. The technique has been tested on different video surveillance scenarios with variable object motion, strong occlusion and change in object scale. Experimental results show that the proposed method not only tracks the object with comparable accuracy to existing particle filter techniques but is up to five times faster. Tracking objects in a multi camera environment is discussed in the final part of the thesis. The ASCPF technique is deployed within a multi-camera environment to track objects across different camera views. Such environments can pose difficult challenges such as changes in object scale and colour features as the objects move from one camera view to another. Variable standard deviation values of the ASCPF have been utilized in order to cope with sudden colour and scale changes. As the object moves from one scene to another, the number of particles, together with the spread value, is increased to a maximum to reduce any effects of scale and colour change. Promising results are obtained when the ASCPF technique is tested on live feeds from four different camera views. It was found that not only did the ASCPF method result in the successful tracking of the moving object across different views but also maintained the real time frame rate due to its reduced computational cost thus indicating that the method is a potential practical solution for multi camera tracking applications

An improved background segmentation method for ghost removals

With ongoing research assessment in higher education and the introduction of master’s‐level work in initial teacher education, the growing need for teacher educators to develop research identities is discussed in relation to mentoring and support in two universities. Twelve interviews—with three teacher educators and three research mentors from each university—were carried out, in order to identify effective mentoring practices and other forms of support, as well as any barriers or problems encountered in developing a research profile. An innovative aspect of the methodological approach is that beginning researchers from the teacher education faculty in both universities undertook the interviewing and co‐authored the article. The need for an entitlement to and protection of research time is stressed, as well as a range of supportive practices within an active research culture. It is argued that this aspect of teacher educators’ professional development requires as much attention as the pedagogical aspects of their rol

PHACT: parallel HOG and correlation tracking

Histogram of Oriented Gradients (HOG) based methods for the detection of humans have become one of the most reliable methods of detecting pedestrians with a single passive imaging camera. However, they are not 100 percent reliable. This paper presents an improved tracker for the monitoring of pedestrians within images. The Parallel HOG and Correlation Tracking (PHACT) algorithm utilises self learning to overcome the drifting problem. A detection algorithm that utilises HOG features runs in parallel to an adaptive and stateful correlator. The combination of both acting in a cascade provides a much more robust tracker than the two components separately could produce. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Isolation and Evaluation of Antibacterial Potential Test of Plant Carthamus oxycantha

The present investigation was initiated to find a suitable alternative to synthetic antibiotics for the management of diseases caused by bacteria. Carthamus oxycantha.L locally known as wild safflower member of family Asteraceae that grows wildly. The study was conducted using as Agar well diffusion to trace the antibacterial potential for to evaluate the efficiency of ethanolic extract of Carthamus oxycantha with concentration of 05, 10, 15, and 20 mg/ml against gram-positive Staphylococcus aureus and gram-negative Escherichia Coli species and them compared with that of Clindamycin, Ampicillin and Kanamycin (10 mg). Zone of inhibition for the extracts were 10.667 to 20.00 mm as compared to standard drug Clindamycin, Ampicillin and kanamycin (15.00-20.00 mm). Antibacterial assays indicates that Carthamus oxycantha has potential natural antimicrobial agents against E-coli and S. aureus. The findings of the present study suggested that ethanolic extract of C. oxycantha has strong potential to serve as possible antibacterial

Impact of Exports on Economic Growth- A Case of Luxemburg

The key purpose of this article is to analyze the significant impact of Exports, Government expenditures and Education expenditures on the economic growth of the developed economy of the Luxemburg, which is the member state of the EU: the biggest exporter in the world. The span of time is from the year 1975 to 2009 on yearly basis with total no. of observations of 35. Present analysis is based on the simple ordinary least square method to indentify the important linkage between the export and the growth considering the economy of Luxemburg. Experimental results reveal a significant positive relationship of exports, government spending, educational expenditure, on growth of the economy. Export shows that one unit increase in the export cause a positive change of .17 in the economic growth. In the same way government, exp. and education exp. show a coefficient of 2.67 and 9.93 with positive sign. This article identifies the association between the export and the economic growth with respect to Luxemburg

Illumination invariant stationary object detection

A real-time system for the detection and tracking of moving objects that becomes stationary in a restricted zone. A new pixel classification method based on the segmentation history image is used to identify stationary objects in the scene. These objects are then tracked using a novel adaptive edge orientation-based tracking method. Experimental results have shown that the tracking technique gives more than a 95% detection success rate, even if objects are partially occluded. The tracking results, together with the historic edge maps, are analysed to remove objects that are no longer stationary or are falsely identified as foreground regions because of sudden changes in the illumination conditions. The technique has been tested on over 7 h of video recorded at different locations and time of day, both outdoors and indoors. The results obtained are compared with other available state-of-the-art methods

On computing linearizing coordinates from the symmetry algebra

A characterization of the symmetry algebra of the N th-order ordinary differential equations (ODEs) with maximal symmetry and all third-order linearizable ODEs is given. This is used to show that such an algebra g determines - up to a point transformation - only one linear equation whose symmetry algebra is g and an algorithmic procedure is given to find the linearizing coordinates. This procedure is applied to several examples from the literature

Computer vision – cloud, smart or both

Bandwidth management and availability is going to improve greatly.The Cloud will become increasingly important for security and computer vision. Integration of Satellite, Fibre, Wireless. Impacts where you do the Computer Visio