Abstract: Multispectral screening systems are becoming more
popular because of their very interesting properties and applications.
One of the most significant applications of multispectral screening
systems is prevention of terrorist attacks. There are many kinds of
threats and many methods of detection. Visual detection of objects
hidden under clothing of a person is one of the most challenging
problems of threats detection. There are various solutions of the
problem; however, the most effective utilize multispectral
surveillance imagers. The development of imaging devices and
exploration of new spectral bands is a chance to introduce new
equipment for assuring public safety. We investigate the possibility
of long lasting detection of potentially dangerous objects covered
with various types of clothing. In the article we present the results of
comparative studies of passive imaging in three spectrums – visible,
infrared and terahertz.
Abstract: Object detection using Wavelet Neural Network (WNN) plays a major contribution in the analysis of image processing. Existing cluster-based algorithm for co-saliency object detection performs the work on the multiple images. The co-saliency detection results are not desirable to handle the multi scale image objects in WNN. Existing Super Resolution (SR) scheme for landmark images identifies the corresponding regions in the images and reduces the mismatching rate. But the Structure-aware matching criterion is not paying attention to detect multiple regions in SR images and fail to enhance the result percentage of object detection. To detect the objects in the high-resolution remote sensing images, Tagged Grid Matching (TGM) technique is proposed in this paper. TGM technique consists of the three main components such as object determination, object searching and object verification in WNN. Initially, object determination in TGM technique specifies the position and size of objects in the current image. The specification of the position and size using the hierarchical grid easily determines the multiple objects. Second component, object searching in TGM technique is carried out using the cross-point searching. The cross out searching point of the objects is selected to faster the searching process and reduces the detection time. Final component performs the object verification process in TGM technique for identifying (i.e.,) detecting the dissimilarity of objects in the current frame. The verification process matches the search result grid points with the stored grid points to easily detect the objects using the Gabor wavelet Transform. The implementation of TGM technique offers a significant improvement on the multi-object detection rate, processing time, precision factor and detection accuracy level.
Abstract: In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.
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: In this paper, a simple moving human detection method is proposed for video surveillance system or access monitoring system. The frame difference and noise threshold are used for initial detection of a moving human-object, and simple labeling method is applied for final human-object segmentation. The simulated results show that the applied algorithm is fast to detect the moving human-objects by performing 95% of correct detection rate. The proposed algorithm has confirmed that can be used as an intelligent video access monitoring system.
Abstract: The image segmentation method described in this
paper has been developed as a pre-processing stage to be used in
methodologies and tools for video/image indexing and retrieval by
content. This method solves the problem of whole objects extraction
from background and it produces images of single complete objects
from videos or photos. The extracted images are used for calculating
the object visual features necessary for both indexing and retrieval
processes.
The segmentation algorithm is based on the cooperation among an
optical flow evaluation method, edge detection and region growing
procedures. The optical flow estimator belongs to the class of
differential methods. It permits to detect motions ranging from a
fraction of a pixel to a few pixels per frame, achieving good results in
presence of noise without the need of a filtering pre-processing stage
and includes a specialised model for moving object detection.
The first task of the presented method exploits the cues from
motion analysis for moving areas detection. Objects and background
are then refined using respectively edge detection and seeded region
growing procedures. All the tasks are iteratively performed until
objects and background are completely resolved.
The method has been applied to a variety of indoor and outdoor
scenes where objects of different type and shape are represented on
variously textured background.
Abstract: 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
Abstract: Based on the combined shape feature and texture
feature, a fast object detection method with rotation invariant features
is proposed in this paper. A quick template matching scheme based
online learning designed for online applications is also introduced in
this paper. The experimental results have shown that the proposed
approach has the features of lower computation complexity and
higher detection rate, while keeping almost the same performance
compared to the HOG-based method, and can be more suitable for
run time applications.
Abstract: 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.
Abstract: We here propose improved version of elastic graph matching (EGM) as a face detector, called the multi-scale EGM (MS-EGM). In this improvement, Gabor wavelet-based pyramid reduces computational complexity for the feature representation often used in the conventional EGM, but preserving a critical amount of information about an image. The MS-EGM gives us higher detection performance than Viola-Jones object detection algorithm of the AdaBoost Haar-like feature cascade. We also show rapid detection speeds of the MS-EGM, comparable to the Viola-Jones method. We find fruitful benefits in the MS-EGM, in terms of topological feature representation for a face.
Abstract: In this work, we present a novel active learning approach
for learning a visual object detection system. Our system
is composed of an active learning mechanism as wrapper around
a sub-algorithm which implement an online boosting-based learning
object detector. In the core is a combination of a bootstrap procedure
and a semi automatic learning process based on the online boosting
procedure. The idea is to exploit the availability of classifier during
learning to automatically label training samples and increasingly
improves the classifier. This addresses the issue of reducing labeling
effort meanwhile obtain better performance. In addition, we propose
a verification process for further improvement of the classifier.
The idea is to allow re-update on seen data during learning for
stabilizing the detector. The main contribution of this empirical study
is a demonstration that active learning based on an online boosting
approach trained in this manner can achieve results comparable or
even outperform a framework trained in conventional manner using
much more labeling effort. Empirical experiments on challenging data
set for specific object deteciton problems show the effectiveness of
our approach.
Abstract: The scattering effect of light in fog improves the
difficulty in visibility thus introducing disturbances in transport
facilities in urban or industrial areas causing fatal accidents or public
harassments, therefore, developing an enhanced fog vision system
with radio wave to improvise the way outs of these severe problems
is really a big challenge for researchers. Series of experimental
studies already been done and more are in progress to know the
weather effect on radio frequencies for different ranges. According to
Rayleigh scattering Law, the propagating wavelength should be
greater than the diameter of the particle present in the penetrating
medium. Direct wave RF signal thus have high chance of failure to
work in such weather for detection of any object. Therefore an
extensive study was required to find suitable region in the RF band
that can help us in detecting objects with proper shape. This paper
produces some results on object detection using 912 MHz band with
successful detection of the persistence of any object coming under the
trajectory of a vehicle navigating in indoor and outdoor environment.
The developed images are finally transformed to video signal to
enable continuous monitoring.
Abstract: Delivering streaming video over wireless is an
important component of many interactive multimedia applications
running on personal wireless handset devices. Such personal devices
have to be inexpensive, compact, and lightweight. But wireless
channels have a high channel bit error rate and limited bandwidth.
Delay variation of packets due to network congestion and the high bit
error rate greatly degrades the quality of video at the handheld
device. Therefore, mobile access to multimedia contents requires
video transcoding functionality at the edge of the mobile network for
interworking with heterogeneous networks and services. Therefore,
to guarantee quality of service (QoS) delivered to the mobile user, a
robust and efficient transcoding scheme should be deployed in
mobile multimedia transporting network. Hence, this paper
examines the challenges and limitations that the video transcoding
schemes in mobile multimedia transporting network face. Then
handheld resources, network conditions and content based mobile
and wireless video transcoding is proposed to provide high QoS
applications. Exceptional performance is demonstrated in the
experiment results. These experiments were designed to verify and
prove the robustness of the proposed approach. Extensive
experiments have been conducted, and the results of various video
clips with different bit rate and frame rate have been provided.
Abstract: 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.
Abstract: In recent years, IT convergence technology has been developed to get creative solution by combining robotics or sports science technology. Object detection and recognition have mainly applied to sports science field that has processed by recognizing face and by tracking human body. But object detection and recognition using vision sensor is challenge task in real world because of illumination. In this paper, object detection and recognition using vision sensor applied to sports simulator has been introduced. Face recognition has been processed to identify user and to update automatically a person athletic recording. Human body has tracked to offer a most accurate way of riding horse simulator. Combined image processing has been processed to reduce illumination adverse affect because illumination has caused low performance in detection and recognition in real world application filed. Face has recognized using standard face graph and human body has tracked using pose model, which has composed of feature nodes generated diverse face and pose images. Face recognition using Gabor wavelet and pose recognition using pose graph is robust to real application. We have simulated using ETRI database, which has constructed on horse riding simulator.
Abstract: Omni directional mobile robots have been popularly
employed in several applications especially in soccer player robots
considered in Robocup competitions. However, Omni directional
navigation system, Omni-vision system and solenoid kicking
mechanism in such mobile robots have not ever been combined. This
situation brings the idea of a robot with no head direction into
existence, a comprehensive Omni directional mobile robot. Such a
robot can respond more quickly and it would be capable for more
sophisticated behaviors with multi-sensor data fusion algorithm for
global localization base on the data fusion. This paper has tried to
focus on the research improvements in the mechanical, electrical and
software design of the robots of team ADRO Iran. The main
improvements are the world model, the new strategy framework,
mechanical structure, Omni-vision sensor for object detection, robot
path planning, active ball handling mechanism and the new kicker
design, , and other subjects related to mobile robot
Abstract: We present a genetic algorithm application to the problem of object registration (i.e., object detection, localization and recognition) in a class of medical images containing various types of blood cells. The genetic algorithm approach taken here is seen to be most appropriate for this type of image, due to the characteristics of the objects. Successful cell registration results on real life microscope images of blood cells show the potential of the proposed approach.
Abstract: This paper presents a novel template-based method to
detect objects of interest from real images by shape matching. To
locate a target object that has a similar shape to a given template
boundary, the proposed method integrates three components: contour
grouping, partial shape matching, and boundary verification. In the
first component, low-level image features, including edges and
corners, are grouped into a set of perceptually salient closed contours
using an extended ratio-contour algorithm. In the second component,
we develop a partial shape matching algorithm to identify the
fractions of detected contours that partly match given template
boundaries. Specifically, we represent template boundaries and
detected contours using landmarks, and apply a greedy algorithm to
search the matched landmark subsequences. For each matched
fraction between a template and a detected contour, we estimate an
affine transform that transforms the whole template into a hypothetic
boundary. In the third component, we provide an efficient algorithm
based on oriented edge lists to determine the target boundary from
the hypothetic boundaries by checking each of them against image
edges. We evaluate the proposed method on recognizing and
localizing 12 template leaves in a data set of real images with clutter
back-grounds, illumination variations, occlusions, and image noises.
The experiments demonstrate the high performance of our proposed
method1.
Abstract: In this paper we present a new method for coin
identification. The proposed method adopts a hybrid scheme using
Eigenvalues of covariance matrix, Circular Hough Transform (CHT)
and Bresenham-s circle algorithm. The statistical and geometrical
properties of the small and large Eigenvalues of the covariance
matrix of a set of edge pixels over a connected region of support are
explored for the purpose of circular object detection. Sparse matrix
technique is used to perform CHT. Since sparse matrices squeeze
zero elements and contain only a small number of non-zero elements,
they provide an advantage of matrix storage space and computational
time. Neighborhood suppression scheme is used to find the valid
Hough peaks. The accurate position of the circumference pixels is
identified using Raster scan algorithm which uses geometrical
symmetry property. After finding circular objects, the proposed
method uses the texture on the surface of the coins called texton,
which are unique properties of coins, refers to the fundamental micro
structure in generic natural images. This method has been tested on
several real world images including coin and non-coin images. The
performance is also evaluated based on the noise withstanding
capability.
Abstract: The objective of this paper is to propose an adaptive multi threshold for image segmentation precisely in object detection. Due to the different types of license plates being used, the requirement of an automatic LPR is rather different for each country. The proposed technique is applied on Malaysian LPR application. It is based on Multi Layer Perceptron trained by back propagation. The proposed adaptive threshold is introduced to find the optimum threshold values. The technique relies on the peak value from the graph of the number object versus specific range of threshold values. The proposed approach has improved the overall performance compared to current optimal threshold techniques. Further improvement on this method is in progress to accommodate real time system specification.