Abstract: The large pose discrepancy is one of the critical
challenges in face recognition during video surveillance. Due to
the entanglement of pose attributes with identity information, the
conventional approaches for pose-independent representation lack
in providing quality results in recognizing largely posed faces. In
this paper, we propose a practical approach to disentangle the pose
attribute from the identity information followed by synthesis of a face
using a classifier network in latent space. The proposed approach
employs a modified generative adversarial network framework
consisting of an encoder-decoder structure embedded with a classifier
in manifold space for carrying out factorization on the latent
encoding. It can be further generalized to other face and non-face
attributes for real-life video frames containing faces with significant
attribute variations. Experimental results and comparison with state
of the art in the field prove that the learned representation of the
proposed approach synthesizes more compelling perceptual images
through a combination of adversarial and classification losses.
Abstract: This paper presents a real time video surveillance system which is capable of tracking multiple real time objects using Polar Vector Median (PVM) and Block Coding Modes (BCM) with Global Motion Compensation (GMC). This strategy works in the packed area and furthermore utilizes the movement vectors and BCM from the compressed bit stream to perform real time object tracking. We propose to do this in view of the neighboring Motion Vectors (MVs) using a method called PVM. Since GM adds to the object’s native motion, for accurate tracking, it is important to remove GM from the MV field prior to further processing. The proposed method is tested on a number of standard sequences and the results show its advantages over some of the current modern methods.
Abstract: Background: Acute gastroenteritis is one of the frequently reported Out-Patient Department (OPD) cases. However, the causative pathogens of these cases are rarely identified at the OPD due to delay in laboratory results or failure to obtain specimens before antibiotics is administered. Method: A retrospective review of surveillance data from the Adentan Municipality, Accra, Ghana that were recorded in the National foodborne disease surveillance system of Ghana, was conducted with the main aim of describing the epidemiology and food practice of cases reported from the Adentan Municipality. The study involved a retrospective review of surveillance data kept on patients who visited health facilities that are involved in foodborne disease surveillance in Ghana, from January 2015 to December 2016. Results: A total of 375 cases were reviewed and these were classified as viral hepatitis (hepatitis A and E), cholera (Vibrio cholerae), dysentery (Shigella sp.), typhoid fever (Salmonella sp.) or gastroenteritis. Cases recorded were all suspected case and the average cases recorded per week was 3. Typhoid fever and dysentery were the two main clinically diagnosed foodborne illnesses. The highest number of cases were observed during the late dry season (Feb to April), which marks the end of the dry season and the beginning of the rainy season. Relatively high number of cases was also observed during the late wet seasons (Jul to Oct) when the rainfall is the heaviest. Home-made food and street vended food were the major sources of suspected etiological food, recording 49.01% and 34.87% of the cases respectively. Conclusion: Majority of cases recorded were classified as gastroenteritis due to the absence of laboratory confirmation. Few cases were classified as typhoid fever and dysentery based on clinical symptoms presented. Patients reporting with foodborne diseases were found to consume home meal and street vended foods as their predominant source of food.
Abstract: A serosurveillance study was conducted to detect the presence of antibodies to African swine fever virus (ASFV) and Classical swine fever virus in pigs sampled from piggeries and Makurdi central slaughter slab in Benue State, Nigeria. 416 pigs from 74 piggeries across 12 LGAs and 44 pigs at the Makurdi central slaughter slab were sampled for serum. The sera collected were analysed using Indirect Enzyme Linked Immunosorbent Assay (ELISA) test kit to test for antibodies to ASFV, while competitive ELISA test kit was used to test for antibodies to CSFV. Of the 416 pigs from piggeries and 44 pigs sampled from the slaughter slab, seven (1.7%) and six (13.6%), respectively, tested positive to ASFV antibodies and was significantly associated (p < 0.0001). Out of the 12 LGAs sampled, Obi LGA had the highest ASFV antibody detection rate of (4.8%) and was significantly associated (p < 0.0001). None of the samples tested positive to CSFV antibodies. The study concluded that antibodies to CSFV were absent in the sampled pigs in piggeries and at the Makurdi central slaughter slab in Benue State, while antibodies to ASFV were present in both locations; hence, the need to keep an eye open for CSF too since both diseases may pose great risk in the study area. Further studies to characterise the ASFV circulating in Benue State and investigate the possible sources is recommended. Routine surveillance to provide a comprehensive and readily accessible data base to plan for the prevention of any fulminating outbreak is also recommended.
Abstract: Fire-related incidents account for extensive loss of life and
material damage. Quick and reliable detection of occurring fires has high
real world implications. Whereas a major research focus lies on the detection
of outdoor fires, indoor camera-based fire detection is still an open issue.
Cameras in combination with computer vision helps to detect flames and
smoke more quickly than conventional fire detectors. In this work, we present
a computer vision-based smoke detection algorithm based on contrast changes
and a multi-step classification. This work accelerates computer vision-based
fire detection considerably in comparison with classical indoor-fire detection.
Abstract: This study aimed to analyse the pregnancy outcomes in patients with TPO positivity after appropriate L-Thyroxin supplementation with close surveillance. All pregnant women attending the antenatal clinic at Milann-The Fertility Center, Bangalore, India- from Aug 2013 to Oct 2014 whose booking TSH was more than 2.5 mIU/L were included along with those pregnant women with prior hypothyroidism who were TPO positive. Those with TPO positive status were vigorously managed with appropriate thyroxin supplementation and the doses were readjusted every 3 to 4 weeks until delivery. Women with recurrent pregnancy loss were also tested for TPO positivity and if tested positive, were monitored serially with TSH and fT4 levels every 3 to 4 weeks and appropriately supplemented with thyroxin when the levels fluctuated. The testing was done after an informed consent in all these women. The statistical software namely SAS 9.2, SPSS 15.0, Stata 10.1, MedCalc 9.0.1, Systat 12.0 and R environment ver.2.11.1 were used for the analysis of the data. 460 pregnant women were screened for thyroid dysfunction at booking of which 52% were hypothyroid. Majority of them (31.08%) were subclinically hypothyroid and the remaining were overt. 25% of the total no. of patients screened were TPO positive. The various pregnancy complications that were observed in the TPO positive women were gestational glucose intolerance [60%], threatened abortion [21%], midtrimester abortion [4.3%], premature rupture of membranes [4.3%], cervical funneling [4.3%] and fetal growth restriction [3.5%]. 95.6% of the patients who followed up till the end delivered beyond 30 weeks. 42.6% of these patients had previous history of recurrent abortions or adverse obstetric outcome and 21.7% of the delivered babies required NICU admission. Obstetric outcomes in our study in terms of midtrimester abortions, placental abruption, and preterm delivery improved for the better after close monitoring of the thyroid hormone [TSH and fT4] levels every 3 to 4 weeks with appropriate dose adjustment throughout pregnancy. Euthyroid women with TPO positive status enrolled in the study incidentally were those with recurrent abortions/infertility and required thyroxin supplements due to elevated Thyroid hormone (TSH, fT4) levels during the course of their pregnancy. Significant associations were found with age>30 years and Hyperhomocysteinemia [p=0.017], recurrent pregnancy loss or previous adverse obstetric outcomes [p=0.067] and APLA [p=0.029]. TPO antibody levels >600 I U/ml were significantly associated with development of gestational hypertension [p=0.041] and fetal growth restriction [p=0.082]. Euthyroid women with TPO positivity were also screened periodically to counter fluctuations of the thyroid hormone levels with appropriate thyroxin supplementation. Thus, early identification along with aggressive management of thyroid dysfunction and stratification of these patients based on their TPO status with appropriate thyroxin supplementation beginning in the first trimester will aid risk modulation and also help avert complications.
Abstract: The aim of this study is to present the results of a retrospective survey on the foreign matter found in foods analyzed at the Adolfo Lutz Institute, from July 2001 to July 2015. All the analyses were conducted according to the official methods described on Association of Official Agricultural Chemists (AOAC) for the micro analytical procedures and Food and Drug Administration (FDA) for the macro analytical procedures. The results showed flours, cereals and derivatives such as baking and pasta products were the types of food where foreign matters were found more frequently followed by condiments and teas. Fragments of stored grains insects, its larvae, nets, excrement, dead mites and rodent excrement were the most foreign matter found in food. Besides, foreign matters that can cause a physical risk to the consumer’s health such as metal, stones, glass, wood were found but rarely. Miscellaneous (shell, sand, dirt and seeds) were also reported. There are a lot of extraneous materials that are considered unavoidable since are something inherent to the product itself, such as insect fragments in grains. In contrast, there are avoidable extraneous materials that are less tolerated because it is preventable with the Good Manufacturing Practice. The conclusion of this work is that although most extraneous materials found in food are considered unavoidable it is necessary to keep the Good Manufacturing Practice throughout the food processing as well as maintaining a constant surveillance of the production process in order to avoid accidents that may lead to occurrence of these extraneous materials in food.
Abstract: Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.
Abstract: In this paper, we present a four-step ortho-rectification
procedure for real-time geo-referencing of video data from a low-cost
UAV equipped with a multi-sensor system. The basic procedures for
the real-time ortho-rectification are: (1) decompilation of the video
stream into individual frames; (2) establishing the interior camera
orientation parameters; (3) determining the relative orientation
parameters for each video frame with respect to each other; (4)
finding the absolute orientation parameters, using a self-calibration
bundle and adjustment with the aid of a mathematical model. Each
ortho-rectified video frame is then mosaicked together to produce a
mosaic image of the test area, which is then merged with a well
referenced existing digital map for the purpose of geo-referencing
and aerial surveillance. A test field located in Abuja, Nigeria was
used to evaluate our method. Video and telemetry data were collected
for about fifteen minutes, and they were processed using the four-step
ortho-rectification procedure. The results demonstrated that the
geometric measurement of the control field from ortho-images is
more accurate when compared with those from original perspective
images when used to pin point the exact location of targets on the
video imagery acquired by the UAV. The 2-D planimetric accuracy
when compared with the 6 control points measured by a GPS receiver
is between 3 to 5 metres.
Abstract: Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).
Abstract: The paper presents an innovative networked radar
system for detection of obstacles in a railway level crossing scenario.
This Monitoring System (MS) is able to detect moving or still
obstacles within the railway level crossing area automatically,
avoiding the need of human presence for surveillance. The MS is also
connected to the National Railway Information and Signaling System
to communicate in real-time the level crossing status. The
architecture is compliant with the highest Safety Integrity Level
(SIL4) of the CENELEC standard. The number of radar sensors used
is configurable at set-up time and depends on how large the level
crossing area can be. At least two sensors are expected and up four
can be used for larger areas. The whole processing chain that
elaborates the output sensor signals, as well as the communication
interface, is fully-digital, was designed in VHDL code and
implemented onto a Xilinx Virtex 6.
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: Urban public spaces are sutured with a range of
surveillance and sensor technologies that claim to enable new forms
of ‘data based citizen participation’, but also increase the tendency
for ‘function-creep’, whereby vast amounts of data are gathered,
stored and analysed in a broad application of urban surveillance. This
kind of monitoring and capacity for surveillance connects with
attempts by civic authorities to regulate, restrict, rebrand and reframe
urban public spaces. A direct consequence of the increasingly
security driven, policed, privatised and surveilled nature of public
space is the exclusion or ‘unfavourable inclusion’ of those considered
flawed and unwelcome in the ‘spectacular’ consumption spaces of
many major urban centres. In the name of urban regeneration,
programs of securitisation, ‘gentrification’ and ‘creative’ and ‘smart’
city initiatives refashion public space as sites of selective inclusion
and exclusion. In this context of monitoring and control procedures,
in particular, children and young people’s use of space in parks,
neighbourhoods, shopping malls and streets is often viewed as a
threat to the social order, requiring various forms of remedial action.
This paper suggests that cities, places and spaces and those who
seek to use them, can be resilient in working to maintain and extend
democratic freedoms and processes enshrined in Marshall’s concept
of citizenship, calling sensor and surveillance systems to account.
Such accountability could better inform the implementation of public
policy around the design, build and governance of public space and
also understandings of urban citizenship in the sensor saturated urban
environment.
Abstract: ICAM-2 (intercellular adhesion molecule 2) or CD102 (Cluster of Differentiation 102) is type I transmembrane glycoproteins, composing 2-9 immunoglobulin-like C2-type domains. ICAM-2 plays the particular role in immune response and cell surveillance. It is concerned in innate and specific immunity, cell survival signal, apoptosis, and anticancer. EST clone of ICAM-2, from P. gigas blood cell EST libraries, showed high identity to human ICAM-2 (92%) with conserve region of ICAM N-terminal domain and part of Ig superfamily. Gene and protein of ICAM-2 has been founded in mammals. This is the first report of ICAM-2 in fish
Abstract: Enhancing the quality of two dimensional signals is one of the most important factors in the fields of video surveillance and computer vision. Usually in real-life video surveillance, false detection occurs due to the presence of random noise, illumination
and shadow artifacts. The detection methods based on background subtraction faces several problems in accurately detecting objects in realistic environments: In this paper, we propose a noise removal algorithm using neighborhood comparison method with thresholding. The illumination variations correction is done in the detected foreground objects by using an amalgamation of techniques like homomorphic decomposition, curvelet transformation and gamma adjustment operator. Shadow is removed using chromaticity estimator with local relation estimator. Results are compared with the existing methods and prove as high robustness in the video surveillance.
Abstract: Automated motion detection and tracking is a challenging task in traffic surveillance. In this paper, a system is developed to gather useful information from stationary cameras for detecting moving objects in digital videos. The moving detection and tracking system is developed based on optical flow estimation together with application and combination of various relevant computer vision and image processing techniques to enhance the process. To remove noises, median filter is used and the unwanted objects are removed by applying thresholding algorithms in morphological operations. Also the object type restrictions are set using blob analysis. The results show that the proposed system successfully detects and tracks moving objects in urban videos.
Abstract: This paper explores the importance of privacy in a
contemporary online world. Crucial to the discussion is the idea of
the Lacanian postmodern fragmented self and the problem of how to
ensure that we have room to fully explore various aspects of our
personalities in an environment which is–or at least feels--safe and
free from observation by others. The paper begins with an
exploration of the idea of the self with particular regard to the ways
in which contemporary life and technology seems to have multiplied
the various faces or masks which we present in different contexts. A
brief history of privacy and surveillance follows. Finally, the paper
ends with an affirmation of the importance of private space as an
essential component of our spiritual and emotional well-being in
today-s wired world.
Abstract: A new target detection technique is presented in this
paper for the identification of small boats in coastal surveillance. The
proposed technique employs an adaptive progressive thresholding (APT) scheme to first process the given input scene to separate any
objects present in the scene from the background. The preprocessing
step results in an image having only the foreground objects, such as
boats, trees and other cluttered regions, and hence reduces the search
region for the correlation step significantly. The processed image is then fed to the shifted phase-encoded fringe-adjusted joint transform
correlator (SPFJTC) technique which produces single and delta-like
correlation peak for a potential target present in the input scene. A
post-processing step involves using a peak-to-clutter ratio (PCR) to determine whether the boat in the input scene is authorized or unauthorized. Simulation results are presented to show that the
proposed technique can successfully determine the presence of an authorized boat and identify any intruding boat present in the given input scene.
Abstract: This paper presents a highly efficient algorithm for detecting and tracking humans and objects in video surveillance sequences. Mean shift clustering is applied on backgrounddifferenced image sequences. For efficiency, all calculations are performed on integral images. Novel corresponding exponential integral kernels are introduced to allow the application of nonuniform kernels for clustering, which dramatically increases robustness without giving up the efficiency of the integral data structures. Experimental results demonstrating the power of this approach are presented.
Abstract: An information procuring and processing emerging technology wireless sensor network (WSN) Consists of autonomous nodes with versatile devices underpinned by applications. Nodes are equipped with different capabilities such as sensing, computing, actuation and wireless communications etc. based on application requirements. A WSN application ranges from military implementation in the battlefield, environmental monitoring, health sector as well as emergency response of surveillance. The nodes are deployed independently to cooperatively monitor the physical and environmental conditions. The architecture of WSN differs based on the application requirements and focus on low cost, flexibility, fault tolerance capability, deployment process as well as conserve energy. In this paper we have present the characteristics, architecture design objective and architecture of WSN