Automatic Surveillance Research Articles

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function. Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e., human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups: normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval. Finally, a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention.

An Automatic Motion Detection System for a Camera Surveillance Video

Objective: To develop image-based alert system when trespassers intrude. Methods: Background subtraction is applied here so that we can detect the motion. In this method, we subtract the present frame from the previous frame which results in the movement or motion as the difference. Using Python as a platform, a frame window can be generated through which the principle can be applied. Findings: In modern era, surveillance is playing an optimum role and has been evolved as a need for industries, companies, shopping malls and even for homes which serves as a security purpose. This project deals with a conventional automatic surveillance system which can be applied without heavy complexity. By this facility, user can know the information even though he is located somewhere else. From the results obtained, one can observe even a small movement effectively from the captured area. Application/Improvement: The proposed model is feasible with minimum maintenance cost. Just Dropbox account which can be accessed from anywhere is enough. Mobile alert can be an alternative.

An Audit on Infection Control Measures in El-Ibrashi's Center of Gastrointestinal Endoscopy - Internal Medicine Department - Cairo University Hospital, Egypt

Background : The beneficial role of gastro-intestinal endoscopy for the prevention, diagnosis, and treatment of many digestive diseases and cancer is well established. Like many sophisticated medical devices, the endoscope is a complex, reusable instrument that requires reprocessing before being used on subsequent patients. Objective : to audit the adherence of El - Ibrashi’s Center to comply with national infection control guidelines and assessment of efficacy of endoscopes disinfection in it. Methodology: The current study was carried out in El - Ibrashi’s Center. It included first: Auditing on adherence of El - Ibrashi’s Center to infection control measures and practices in relation to infection control standards in endoscopy units and assessment of efficacy of endoscopes disinfection through a survey study. Results : in this study we found that a total of 9.6% of accessory channels samples were contaminated after disinfection. Pseudomonas aeruginosa and coagulase negative staphylococci (CONS) were the most frequently microorganisms isolated from endoscope accessory channel samples The results of water samples used for endoscopic cleaning showed that 33.3% of water samples taken from connected water bottles were contaminated and Pseudomonas aeruginosa was the most frequent contaminant and inefficient reprocessing mainly due to use of tap water for final rinsing without alcohol rinse or forced air drying and defective storage. Conclusion : This study revealed that the compliances were not good in the unit, the compliance with the quality control measures there was a defect in monitoring the effective concentration of the used disinfectant daily, absence of filters for final rinse water in automatic reprocessors and microbiological surveillance tests were not done routinely at regular intervals as recommended by different guidelines. Finally there were some defects in the design of procedure and reprocessing rooms.

A case-based reasoning system for aiding detection and classification of nosocomial infections

Nowadays, it is recognized worldwide that healthcare-associated infections are responsible for an increase in patient morbidity, mortality, and higher costs related to prolonged hospital stays. As electronic health data are increasingly available today, there is a unique opportunity to implement real-time decision support systems for automating the surveillance of healthcare-associated infections. As a consequence, different electronic surveillance systems have been implemented to date with varying degrees of success. However, there have been few instances in which clinical data and physician narratives with the potential to significantly improve electronic surveillance alternatives have been adopted. In this context, the present work introduces a case-based reasoning system for the automatic surveillance and diagnosis of healthcare-associated infections. The developed system makes use of different machine learning techniques in order to (i) automatically extract evidence from different types of data including clinical unstructured documents, (ii) incorporate static a priori knowledge handled by infection preventionists, and (iii) dynamically generate new knowledge as well as understandable explanations about the system's decisions. Results obtained from a real deployment in a public hospital belonging to the Spanish National Health System trained with 2569 samples belonging to 1800 patients during more than 10 consecutive months recognize the usefulness of the system.

SIFT and Tensor Based Object Detection and Classification in Videos Using Deep Neural Networks

Object classification in videos is very important for applications in automatic visual surveillance system. The process of classifying objects into predefined and semantically meaningful categories using its features is called object classification. As far as humans are concerned object classification in videos is a simple task but it is a complex and challenging task for machines due to different factors such as object size, occlusion, scaling, lightening etc. The need for analyzing video sequences resulted in the development of different object classification techniques. In this paper we propose a new model for detection and classification of objects in videos by incorporating Tensor features along with SIFT to classify the detected objects using Deep Neural Network(DNN. Deep Neural Networks are capable of handling large higher dimensional data with billions of parameters as like human brain. Simulation results obtained illustrate that the proposed classifier model produces more accurate results than the existing methods, which combines both SIFT and tensor features for feature extraction and DNN for classification.

Prediction Performance of Support Vector Machines with Fused Data in Road Scene Analysis

Automatic video-based vehicle detection is one of the main research topics in Intelligent Transportation Systems (ITS) and is a key element for automatic traffic surveillance systems. Support Vector Machines (SVMs) have been increasingly applied to an automatic video-based vehicle detection and a road scene analysis because of their remarkable performance in prediction accuracy. The property of input data for learning on SVMs determines the predictive performance. It is important task to choose the best input vectors in order to improve the predictive performance. It is normal to use a single property of input vectors in the application of learning models. However, the composition of different input vectors may affect predictive performance and a new input vector will be created by combining two raw data. In this paper, two information sources of edge information and pixel gray value have been combined to detect vehicle in road scene images and see how the fused data affect the predictive performance in SVMs. The experimental results of this study show that the fused data may provide better performance in predictive accuracy than a raw input data. Moreover, the results show that SVMs could provide much better performance than the Backpropagation model which is the best known neural network.

Integrated active sensor system for real time vibration monitoring.

We report a self-powered, lightweight and cost-effective active sensor system for vibration monitoring with multiplexed operation based on contact electrification between sensor and detected objects. The as-fabricated sensor matrix is capable of monitoring and mapping the vibration state of large amounts of units. The monitoring contents include: on-off state, vibration frequency and vibration amplitude of each unit. The active sensor system delivers a detection range of 0–60 Hz, high accuracy (relative error below 0.42%), long-term stability (10000 cycles). On the time dimension, the sensor can provide the vibration process memory by recording the outputs of the sensor system in an extend period of time. Besides, the developed sensor system can realize detection under contact mode and non-contact mode. Its high performance is not sensitive to the shape or the conductivity of the detected object. With these features, the active sensor system has great potential in automatic control, remote operation, surveillance and security systems.

Is hospital information system relevant to detect surgical site infection? Findings from a prospective surveillance study in posterior instrumented spinal surgery

ObjectSpinal instrumentation has a high rate of surgical site infection (SSI), but results greatly vary depending on surveillance methodology, surgical procedures, or quality of follow-up. Our aim was to study true incidence of SSI in spinal surgery by significant data collection, and to compare it with the results obtained through the hospital information system. MethodsThis work is a single center prospective cohort study that included all patients consecutively operated on for spinal instrumentation by posterior approach over a six-month period regardless the etiology. For all patients, a “high definition” prospective method of surveillance was performed by the infection control (IC) department during at least 12months after surgery. Results were then compared with findings from automatic surveillance though the hospital information system (HIS). ResultsOne hundred and fifty-four patients were included. We found no hardly difference between “high definition” and automatic surveillance through the HIS, even if HIS tended to under-estimate the infection rate: rate of surgical site infection was 2.60% and gross SSI incidence rate via the hospital information system was 1.95%. Smoking and alcohol consumption were significantly related to a SSI. ConclusionOur SSI rates to reflect the true incidence of infectious complications in posterior instrumented adult spinal surgery in our hospital and these results were consistent with the lower levels of published infection rate. In-house surveillance by surgeons only is insufficiently sensitive. Further studies with more patients and a longer inclusion time are needed to conclude if SSI case detection through the HIS could be a relevant and effective alternative method.

Cross border intruder detection in hilly terrain in dark environment

Automatic surveillance is an important research area and has been studied for many years. In this paper, a new method of Cross Border Intrusion detection in hilly region has been discussed. The Kinect camera ensures that intruders crossing the border can be detected during daytime as well as in night. We introduce a border surveillance system that is able to recognize intruder actions like standing, walking, crawling, and bending, etc. in illuminated as well as in dark conditions. The system is able to detect whether the moving object is a human being or an animal and activates an alarm if it detects human movement. Also, the system works well in plain as well as in hilly terrain. Using skeletal tracking application provided by Kinect console, the actions are classified and recognized. The HMM based classification makes the system robust and thus, makes it a versatile component for being a part of other different applications. The proposed system gives result for an overall detection accuracy of 92%.

Hybrid Object Detection and Image Recovery Techniques

the change of time arise the need to work on image processing. This paper gives work on two aspects of image. One Recovery or reconstruction of image and at the other end it is for object detection.This paper investigates the possibility of reconstruction of images. This paper is concerned with a removing corruption of an image and remove problem in image. We propose a new algorithm for solving one of the image restorations and reconstruction algorithm. At another end we also concentrate on object detection. The principal component of image analysis technique is image matching. This principal component became of extreme importance in different area of applications such as in navigation, guidance, automatic surveillance, robot vision. Experiments on a set of image restoration and reconstruction along with object detection show that proposed algorithm performs well enough.

A Web-Based, Hospital-Wide Health Care-Associated Bloodstream Infection Surveillance and Classification System: Development and Evaluation.

BackgroundSurveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent.ObjectiveTo develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system.MethodsWe developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs.ResultsIn this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20; r=.999 P<.001) and by time (n=14; r=.941; P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days.ConclusionsThis system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system.

A Framework for Automatic Video Surveillance Indexing and Retrieval

The manual search through the surveillance video archives for a specific object or event is very time-consuming and tedious task due to the large volume of video data captured by many installed surveillance cameras. Therefore, the solution to accelerate and facilitate this process is to design an automatic video surveillance with the efficient and effective video indexing, video data model, query formulation and language, as well as visualization interface. There are many challenges, for developing a powerful query processing module, formulating complex queries and selecting suitable similarity matching strategy to detect any abnormality based on semantic content of the video using various query types. This study presents a novel video surveillance indexing and retrieval framework to cope with the above challenges. The proposed framework consists of three main modules i.e., pre-processing, query processing and retrieval processing. Moreover, it supports an efficient search and actively refines the retrieval result by formulating various query types including: query-by-text, query-by-example and query-by-region.

Automatic surveillance in transportation hubs: No longer just about catching the bad guy

As critical infrastructure such as transportation hubs continue to grow in complexity, greater importance is placed on monitoring these facilities to ensure their secure and efficient operation. In order to achieve these goals, technology continues to evolve in response to the needs of various infrastructure. To date, however, the focus of technology for surveillance has been primarily concerned with security, and little attention has been placed on assisting operations and monitoring performance in real-time. Consequently, solutions have emerged to provide real-time measurements of queues and crowding in spaces, but have been installed as system add-ons (rather than making better use of existing infrastructure), resulting in expensive infrastructure outlay for the owner/operator, and an overload of surveillance systems which in itself creates further complexity. Given many critical infrastructure already have camera networks installed, it is much more desirable to better utilise these networks to address operational monitoring as well as security needs.Recently, a growing number of approaches have been proposed to monitor operational aspects such as pedestrian throughput, crowd size and dwell times. In this paper, we explore how these techniques relate to and complement the more commonly seen security analytics, and demonstrate the value that can be added by operational analytics by demonstrating their performance on airport surveillance data. We explore how multiple analytics and systems can be combined to better leverage the large amount of data that is available, and we discuss the applicability and resulting benefits of the proposed framework for the ongoing operation of airports and airport networks.

Automatic classification of diseases from free-text death certificates for real-time surveillance.

BackgroundDeath certificates provide an invaluable source for mortality statistics which can be used for surveillance and early warnings of increases in disease activity and to support the development and monitoring of prevention or response strategies. However, their value can be realised only if accurate, quantitative data can be extracted from death certificates, an aim hampered by both the volume and variable nature of certificates written in natural language. This study aims to develop a set of machine learning and rule-based methods to automatically classify death certificates according to four high impact diseases of interest: diabetes, influenza, pneumonia and HIV.MethodsTwo classification methods are presented: i) a machine learning approach, where detailed features (terms, term n-grams and SNOMED CT concepts) are extracted from death certificates and used to train a set of supervised machine learning models (Support Vector Machines); and ii) a set of keyword-matching rules. These methods were used to identify the presence of diabetes, influenza, pneumonia and HIV in a death certificate. An empirical evaluation was conducted using 340,142 death certificates, divided between training and test sets, covering deaths from 2000–2007 in New South Wales, Australia. Precision and recall (positive predictive value and sensitivity) were used as evaluation measures, with F-measure providing a single, overall measure of effectiveness. A detailed error analysis was performed on classification errors.ResultsClassification of diabetes, influenza, pneumonia and HIV was highly accurate (F-measure 0.96). More fine-grained ICD-10 classification effectiveness was more variable but still high (F-measure 0.80). The error analysis revealed that word variations as well as certain word combinations adversely affected classification. In addition, anomalies in the ground truth likely led to an underestimation of the effectiveness.ConclusionsThe high accuracy and low cost of the classification methods allow for an effective means for automatic and real-time surveillance of diabetes, influenza, pneumonia and HIV deaths. In addition, the methods are generally applicable to other diseases of interest and to other sources of medical free-text besides death certificates.

A behavior-based flame detection method for a real-time video surveillance system

Some characteristics of flames, including vague shapes and fire-like colors, are irregular, so it is difficult to use a video camera for real-time flame detection. To achieve fully automatic surveillance of fires, a real-time incipient flame detection approach that uses three stages of video processing is proposed in this study. Firstly, a thumbnail method is applied to the flame image for the various resolutions for real-time processing. A median filter and a Gaussian filter are used to remove the image noise. The flame edge information in the image is then enhanced, using a sharpening filter. Secondly, quasi-periodic behavior in flame boundaries is detected using motion detection and a background edge model, so some candidates for real flames can be selected. Thirdly, an evaluation method that uses four criteria, including compactness, fill rate, corner flicker rate and growth rate is used to identify the correct flame. The results are verified using videos of complex scenes at different resolutions. A comparison with other systems is made, to demonstrate that the proposed method is more accurate.

Acoustic event filterbank for enabling robust event recognition by cleaning robot

Due to its mobile capability when performing house-cleaning function in absence of home owners, a cleaning robot has sufficient capacity to be fully utilized as an automatic surveillance system for indoor security. While many research efforts have been made recently to provide a robot understand the auditory environment, there are still many obstacles to overcome. One of the most serious challenges encountered in providing accurate auditory scene analysis is the presence of robot ego noise. Robot ego noise is primarily generated by the embedded motors on a robot during its operation. This paper proposes a new filterbank design based on discriminative distances within event-to-noise and event-to-event, respectively. The proposed filterbank essentially is designed to provide reliable recognition under cleaning robot ego noise through the result of event spectrum analysis. The experimental results indicate that the features extracted by the proposed filterbank are more robust than the conventional ones.

Kinematic-based Markerless Human Tracking in 3D Probabilistic Occupancy Grids

Markerless human motion tracking can be employed in many applications such as automatic surveillance, motion capture, human-machine interface and activity recognition. This problem has been extensively studied in the computer vision research community in the last years. In this context, the present paper presents an approach for 3D markerless human motion tracking based on a skeletal kinematic model of the human body. This method is applied over a 3D probabilistic occupancy grid of the environment, which is constructed by means of a Bayesian fusion of images from multiple synchronized sensoring cameras. Although the use of kinematic models in 3D human tracking is widely employed, its use over 3D probabilistic occupancy grids still was not vastly investigated in the literature. The experiments were performed using a public dataset with video sequences of people in motion. The results show that the method is capable of dealing adequately with the 3D markerless human motion tracking problem.

Recent survey on crowd density estimation and counting for visual surveillance

Automated crowd density estimation and counting are popular and important topic in crowd analysis. The last decades witnessed different of many significant publications in this field and it has been and still a challenging problem for automatic visual surveillance over many years. This paper presents a survey on crowd density estimation and counting methods employed for visual surveillance in the perspective of computer vision research. This survey covers two main approaches which are direct approach (i.e., object based target detection) and indirect approach (e.g. pixel-based, texture-based, and corner points based analysis). This review categorizes and delineates several crowd density estimation and counting methods that have been applied for the examination of crowd scenes.

Infrared skin temperature measurements for monitoring health in pigs: a review.

Infrared temperature measurement equipment (IRTME) is gaining popularity as a diagnostic tool for evaluating human and animal health. It has the prospect of reducing subject stress and disease spread by being implemented as an automatic surveillance system and by a quick assessment of skin temperatures without need for restraint or contact. This review evaluates studies and applications where IRTME has been used on pigs. These include investigations of relationships between skin, ambient and body temperatures and applications for detecting fever, inflammation, lesions, ovulation, and stress as well as for meat quality assessment. The best skin locations for high correlation between skin temperature and rectal temperature are most likely thermal windows such as ear base, eye region and udder. However, this may change with age, stressors, and biological state changes, for example, farrowing. The studies performed on pigs using IRTME have presented somewhat discrepant results, which could be caused by inadequate equipment, varying knowledge about reliable equipment operation, and site-specific factors not included in the assessment. Future focus areas in the field of IRTME are suggested for further development of new application areas and increased diagnostic value in the porcine and animal setting in general.

A multimodal approach for individual tracking of people and their belongings

In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in visible images) is shown to result in better object detection performance. We use adaptive background modeling and local intensity operation for object detection and the mean-shift tracking algorithm for fully automatic tracking. Trackers are refreshed to resolve potential problems which may occur due to the changes in object’s size, shape and to handle occlusion-split and to detect newly emerging objects as well as objects that leave the scene. The proposed scheme is applied to the abandoned object detection problem and the results are compared with the state of art methods. The results show that the proposed method facilitate individual tracking of objects for various applications, and provide lower false alarm rates compared to the state of art methods when applied to the abandoned object detection problem.