Adversarial Disentanglement Using Latent Classifier for Pose-Independent Representation
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.
Real Time Object Tracking in H.264/ AVC Using Polar Vector Median and Block Coding Modes
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.
Surveillance for African Swine Fever and Classical Swine Fever in Benue State, Nigeria
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.
Description of Reported Foodborne Diseases in Selected Communities within the Greater Accra Region-Ghana: Epidemiological Review of Surveillance Data
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.
Video Based Ambient Smoke Detection By Detecting Directional Contrast Decrease
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.
Occurrence of Foreign Matter in Food: Applied Identification Method - Association of Official Agricultural Chemists (AOAC) and Food and Drug Administration (FDA)
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.
Efficient Utilization of Unmanned Aerial Vehicle (UAV) for Fishing through Surveillance for Fishermen
UAV’s are small remote operated or automated aerial
surveillance systems without a human pilot aboard. UAV’s generally
finds its use in military and special operation application, a recent
growing trend in UAV’s finds its application in several civil and nonmilitary
works such as inspection of power or pipelines. The
objective of this paper is the augmentation of a UAV in order to
replace the existing expensive sonar (Sound Navigation And
Ranging) based equipment amongst small scale fisherman, for whom
access to sonar equipment are restricted due to limited economic
resources. The surveillance equipment’s present in the UAV will
relay data and GPS (Global Positioning System) location onto a
receiver on the fishing boat using RF signals, using which the
location of the schools of fishes can be found. In addition to this, an
emergency beacon system is present for rescue operations and drone
Automatic Motion Trajectory Analysis for Dual Human Interaction Using Video Sequences
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.).
Toward Indoor and Outdoor Surveillance Using an Improved Fast Background Subtraction Algorithm
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
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
Urban Citizenship in a Sensor Rich Society
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
H.263 Based Video Transceiver for Wireless Camera System
In this paper, a design of H.263 based wireless video
transceiver is presented for wireless camera system. It uses standard
WIFI transceiver and the covering area is up to 100m. Furthermore the
standard H.263 video encoding technique is used for video
compression since wireless video transmitter is unable to transmit high
capacity raw data in real time and the implemented system is capable
of streaming at speed of less than 1Mbps using NTSC 720x480 video.
The Masquerade of Life, Our Many Selves and Issues of Privacy
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.
Anomaly Based On Frequent-Outlier for Outbreak Detection in Public Health Surveillance
Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.
Zigbee Based Wireless Energy Surveillance System for Energy Savings
In this paper, zigbee communication based wireless energy surveillance system is presented. The proposed system consists of multiple energy surveillance devices and an energy surveillance monitor. Each different standby power-off value of electric device is set automatically by using learning function of energy surveillance device. Thus adaptive standby power-off function provides user convenience and it maximizes the energy savings. Also, power consumption monitoring function is helpful to reduce inefficient energy consumption in home. The zigbee throughput simulator is designed to evaluate minimum transmission power and maximum allowable information quantity in the proposed system. The test result of prototype has been satisfied all the requirements. The proposed system has confirmed that can be used as an intelligent energy surveillance system for energy savings in home or office.
ADABeV: Automatic Detection of Abnormal Behavior in Video-surveillance
Intelligent Video-Surveillance (IVS) systems are
being more and more popular in security applications. The analysis
and recognition of abnormal behaviours in a video sequence has
gradually drawn the attention in the field of IVS, since it allows
filtering out a large number of useless information, which guarantees
the high efficiency in the security protection, and save a lot of human
and material resources. We present in this paper ADABeV, an
intelligent video-surveillance framework for event recognition in
crowded scene to detect the abnormal human behaviour. This
framework is attended to be able to achieve real-time alarming,
reducing the lags in traditional monitoring systems. This architecture
proposal addresses four main challenges: behaviour understanding in
crowded scenes, hard lighting conditions, multiple input kinds of
sensors and contextual-based adaptability to recognize the active
context of the scene.
Digital Sites- Performative Views
Webcam systems now function as the new privileged
vantage points from which to view the city. This transformation of
CCTV technology from surveillance to promotional tool is significant
because its'scopic regime' presents, back to the public, a new virtual
'site' that sits alongside its real-time counterpart. Significantly,
thisraw 'image' data can, in fact,be co-optedand processed so as to
disrupt their original purpose. This paper will demonstrate this
disruptive capacity through an architectural project. It will reveal how
the adaption the webcam image offers a technical springboard by
which to initiate alternate urban form making decisions and subvert
the disciplinary reliance on the 'flat' orthographic plan. In so doing,
the paper will show how this 'digital material' exceeds the imagistic
function of the image; shiftingit from being a vehicle of signification
to a site of affect.
Object Detection based Weighted-Center Surround Difference
Intelligent traffic surveillance technology is an issue in
the field of traffic data analysis. Therefore, we need the technology to
detect moving objects in real-time while there are variations in background and natural light. In this paper, we proposed a Weighted-Center Surround Difference
method for object detection in outdoor environments. The proposed system detects objects using the saliency map that is obtained by
analyzing the weight of each layers of Gaussian pyramid. In order to validate the effectiveness of our system, we implemented the proposed
method using a digital signal processor, TMS320DM6437.
Experimental results show that blurred noisy around objects was effectively eliminated and the object detection accuracy is improved.
A Vehicular Visual Tracking System Incorporating Global Positioning System
Surveillance system is widely used in the traffic
monitoring. The deployment of cameras is moving toward a
ubiquitous camera (UbiCam) environment. In our previous study, a
novel service, called GPS-VT, was firstly proposed by incorporating
global positioning system (GPS) and visual tracking techniques for
the UbiCam environment. The first prototype is called GODTA
(GPS-based Moving Object Detection and Tracking Approach). For a
moving person carried GPS-enabled mobile device, he can be
tracking when he enters the field-of-view (FOV) of a camera
according to his real-time GPS coordinate. In this paper, GPS-VT
service is applied to the tracking of vehicles. The moving speed of a
vehicle is much faster than a person. It means that the time passing
through the FOV is much shorter than that of a person. Besides, the
update interval of GPS coordinate is once per second, it is
asynchronous with the frame rate of the real-time image. The above
asynchronous is worsen by the network transmission delay. These
factors are the main challenging to fulfill GPS-VT service on a
vehicle.In order to overcome the influence of the above factors, a
back-propagation neural network (BPNN) is used to predict the
possible lane before the vehicle enters the FOV of a camera. Then, a
template matching technique is used for the visual tracking of a target
vehicle. The experimental result shows that the target vehicle can be
located and tracking successfully. The success location rate of the
implemented prototype is higher than that of the previous GODTA.
Traffic Density Estimation for Multiple Segment Freeways
Traffic density, an indicator of traffic
conditions, is one of the most critical characteristics to
Intelligent Transport Systems (ITS). This paper investigates
recursive traffic density estimation using the information
provided from inductive loop detectors. On the basis of the
phenomenological relationship between speed and density, the
existing studies incorporate a state space model and update the
density estimate using vehicular speed observations via the
extended Kalman filter, where an approximation is made
because of the linearization of the nonlinear observation
equation. In practice, this may lead to substantial estimation
errors. This paper incorporates a suitable transformation to
deal with the nonlinear observation equation so that the
approximation is avoided when using Kalman filter to
estimate the traffic density. A numerical study is conducted. It
is shown that the developed method outperforms the existing
methods for traffic density estimation.
Smart Surveillance using PDA
The aim of this research is to develop a fast and
reliable surveillance system based on a personal digital assistant
(PDA) device. This is to extend the capability of the device to detect
moving objects which is already available in personal computers.
Secondly, to compare the performance between Background
subtraction (BS) and Temporal Frame Differencing (TFD) techniques
for PDA platform as to which is more suitable. In order to reduce
noise and to prepare frames for the moving object detection part,
each frame is first converted to a gray-scale representation and then
smoothed using a Gaussian low pass filter. Two moving object
detection schemes i.e., BS and TFD have been analyzed. The
background frame is updated by using Infinite Impulse Response
(IIR) filter so that the background frame is adapted to the varying
illuminate conditions and geometry settings. In order to reduce the
effect of noise pixels resulting from frame differencing
morphological filters erosion and dilation are applied. In this
research, it has been found that TFD technique is more suitable for
motion detection purpose than the BS in term of speed. On average
TFD is approximately 170 ms faster than the BS technique
Real Time Detection, Tracking and Recognition of Medication Intake
In this paper, the detection and tracking of face, mouth, hands and medication bottles in the context of medication intake monitoring with a camera is presented. This is aimed at recognizing medication intake for elderly in their home setting to avoid an inappropriate use. Background subtraction is used to isolate moving objects, and then, skin and bottle segmentations are done in the RGB normalized color space. We use a minimum displacement distance criterion to track skin color regions and the R/G ratio to detect the mouth. The color-labeled medication bottles are simply tracked based on the color space distance to their mean color vector. For the recognition of medication intake, we propose a three-level hierarchal approach, which uses activity-patterns to recognize the normal medication intake activity. The proposed method was tested with three persons, with different medication intake scenarios, and gave an overall precision of over 98%.
Real-time Target Tracking Using a Pan and Tilt Platform
In recent years, we see an increase of interest for efficient tracking systems in surveillance applications. Many of the proposed techniques are designed for static cameras environments. When the camera is moving, tracking moving objects become more difficult and many techniques fail to detect and track the desired targets. The problem becomes more complex when we want to track a specific object in real-time using a moving Pan and Tilt camera system to keep the target within the image. This type of tracking is of high importance in surveillance applications. When a target is detected at a certain zone, the possibility of automatically tracking it continuously and keeping it within the image until action is taken is very important for security personnel working in very sensitive sites. This work presents a real-time tracking system permitting the detection and continuous tracking of targets using a Pan and Tilt camera platform. A novel and efficient approach for dealing with occlusions is presented. Also a new intelligent forget factor is introduced in order to take into account target shape variations and avoid learning non desired objects. Tests conducted in outdoor operational scenarios show the efficiency and robustness of the proposed approach.
Scenario Recognition in Modern Building Automation
Modern building automation needs to deal with very
different types of demands, depending on the use of a building and the
persons acting in it. To meet the requirements of situation awareness
in modern building automation, scenario recognition becomes more
and more important in order to detect sequences of events and to react
to them properly. We present two concepts of scenario recognition
and their implementation, one based on predefined templates and the
other applying an unsupervised learning algorithm using statistical
methods. Implemented applications will be described and their advantages
and disadvantages will be outlined.
MATLAB-based System for Centralized Monitoring and Self Restoration against Fiber Fault in FTTH
This paper presented a MATLAB-based system named Smart Access Network Testing, Analyzing and Database (SANTAD), purposely for in-service transmission surveillance and self restoration against fiber fault in fiber-to-the-home (FTTH) access network. The developed program will be installed with optical line terminal (OLT) at central office (CO) to monitor the status and detect any fiber fault that occurs in FTTH downwardly from CO towards residential customer locations. SANTAD is interfaced with optical time domain reflectometer (OTDR) to accumulate every network testing result to be displayed on a single computer screen for further analysis. This program will identify and present the parameters of each optical fiber line such as the line's status either in working or nonworking condition, magnitude of decreasing at each point, failure location, and other details as shown in the OTDR's screen. The failure status will be delivered to field engineers for promptly actions, meanwhile the failure line will be diverted to protection line to ensure the traffic flow continuously. This approach has a bright prospect to improve the survivability and reliability as well as increase the efficiency and monitoring capabilities in FTTH.
Interactive PTZ Camera Control System Using Wii Remote and Infrared Sensor Bar
This paper proposes an alternative control mechanism
for an interactive Pan/Tilt/Zoom (PTZ) camera control system.
Instead of using a mouse or a joystick, the proposed mechanism
utilizes a Nintendo Wii remote and infrared (IR) sensor bar. The Wii
remote has buttons that allows the user to control the movement of a
PTZ camera through Bluetooth connectivity. In addition, the Wii
remote has a built-in motion sensor that allows the user to give
control signals to the PTZ camera through pitch and roll movement.
A stationary IR sensor bar, placed at some distance away opposite the
Wii remote, enables the detection of yaw movement. In addition, the
Wii remote-s built-in IR camera has the ability to detect its spatial
position, and thus generates a control signal when the user moves the
Wii remote. Some experiments are carried out and their performances
are compared with an industry-standard PTZ joystick.
Efficient Mean Shift Clustering Using Exponential Integral Kernels
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.
Hot-Spot Blob Merging for Real-Time Image Segmentation
One of the major, difficult tasks in automated video
surveillance is the segmentation of relevant objects in the scene.
Current implementations often yield inconsistent results on average
from frame to frame when trying to differentiate partly occluding
objects. This paper presents an efficient block-based segmentation
algorithm which is capable of separating partly occluding objects and
detecting shadows. It has been proven to perform in real time with a
maximum duration of 47.48 ms per frame (for 8x8 blocks on a
720x576 image) with a true positive rate of 89.2%. The flexible
structure of the algorithm enables adaptations and improvements with
little effort. Most of the parameters correspond to relative differences
between quantities extracted from the image and should therefore not
depend on scene and lighting conditions. Thus presenting a
performance oriented segmentation algorithm which is applicable in
all critical real time scenarios.
Shadow Detection for Increased Accuracy of Privacy Enhancing Methods in Video Surveillance Edge Devices
Shadow detection is still considered as one of the
potential challenges for intelligent automated video surveillance
systems. A pre requisite for reliable and accurate detection and
tracking is the correct shadow detection and classification. In such a
landscape of conditions, privacy issues add more and more
complexity and require reliable shadow detection.
In this work the intertwining between security, accuracy,
reliability and privacy is analyzed and, accordingly, a novel
architecture for Privacy Enhancing Video Surveillance (PEVS) is
introduced. Shadow detection and masking are dealt with through the
combination of two different approaches simultaneously. This results
in a unique privacy enhancement, without affecting security.
Subsequently, the methodology was employed successfully in a
large-scale wireless video surveillance system; privacy relevant
information was stored and encrypted on the unit, without
transferring it over an un-trusted network.
Implementation of a Motion Detection System
In today-s competitive environment, the security concerns have grown tremendously. In the modern world, possession is known to be 9/10-ths of the law. Hence, it is imperative for one to be able to safeguard one-s property from worldly harms such as thefts, destruction of property, people with malicious intent etc. Due to the advent of technology in the modern world, the methodologies used by thieves and robbers for stealing have been improving exponentially. Therefore, it is necessary for the surveillance techniques to also improve with the changing world. With the improvement in mass media and various forms of communication, it is now possible to monitor and control the environment to the advantage of the owners of the property. The latest technologies used in the fight against thefts and destruction are the video surveillance and monitoring. By using the technologies, it is possible to monitor and capture every inch and second of the area in interest. However, so far the technologies used are passive in nature, i.e., the monitoring systems only help in detecting the crime but do not actively participate in stopping or curbing the crime while it takes place. Therefore, we have developed a methodology to detect the motion in a video stream environment and this is an idea to ensure that the monitoring systems not only actively participate in stopping the crime, but do so while the crime is taking place. Hence, a system is used to detect any motion in a live streaming video and once motion has been detected in the live stream, the software will activate a warning system and capture the live streaming video.
A New Method for Detection of Artificial Objects and Materials from Long Distance Environmental Images
The article presents a new method for detection of
artificial objects and materials from images of the environmental
(non-urban) terrain. Our approach uses the hue and saturation (or Cb
and Cr) components of the image as the input to the segmentation
module that uses the mean shift method. The clusters obtained as the
output of this stage have been processed by the decision-making
module in order to find the regions of the image with the significant
possibility of representing human. Although this method will detect
various non-natural objects, it is primarily intended and optimized for
detection of humans; i.e. for search and rescue purposes in non-urban
terrain where, in normal circumstances, non-natural objects shouldn-t
be present. Real world images are used for the evaluation of the