Abstract: Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.
Abstract: This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.
Abstract: This paper presents the development of a robot car
that can track the motion of an object by detecting its color through
an Android device. The employed computer vision algorithm uses the
OpenCV library, which is embedded into an Android application of a
smartphone, for manipulating the captured image of the object. The
captured image of the object is subjected to color conversion and is
transformed to a binary image for further processing after color
filtering. The desired object is clearly determined after removing
pixel noise by applying image morphology operations and contour
definition. Finally, the area and the center of the object are
determined so that object’s motion to be tracked. The smartphone
application has been placed on a robot car and transmits by Bluetooth
to an Arduino assembly the motion directives so that to follow
objects of a specified color. The experimental evaluation of the
proposed algorithm shows reliable color detection and smooth
tracking characteristics.
Abstract: The inspection of underneath vehicle system has been
given significant attention by governments after the threat of
terrorism become more prevalent. New technologies such as mobile
robots and computer vision are led to have more secure environment.
This paper proposed that a mobile robot like Aria robot can be used
to search and inspect the bombs under parking a lot vehicle. This
robot is using fuzzy logic and subsumption algorithms to control the
robot that movies underneath the vehicle. An OpenCV library and
laser Hokuyo are added to Aria robot to complete the experiment for
under vehicle inspection. This experiment was conducted at the
indoor environment to demonstrate the efficiency of our methods to
search objects and control the robot movements under vehicle. We
got excellent results not only by controlling the robot movement but
also inspecting object by the robot camera at same time. This success
allowed us to know the requirement to construct a new cost effective
robot with more functionality.
Abstract: Eyes are an essential and conspicuous organ of the human body. Human eyes are outward and inward portals of the body that allows to see the outside world and provides glimpses into ones inner thoughts and feelings. Inevitable blindness and visual impairments may results from eye-related disease, trauma, or congenital or degenerative conditions that cannot be corrected by conventional means. The study emphasizes innovative tools that will serve as an aid to the blind and visually impaired (VI) individuals. The researchers fabricated a prototype that utilizes the Microsoft Kinect for Windows and Arduino microcontroller board. The prototype facilitates advanced gesture recognition, voice recognition, obstacle detection and indoor environment navigation. Open Computer Vision (OpenCV) performs image analysis, and gesture tracking to transform Kinect data to the desired output. A computer vision technology device provides greater accessibility for those with vision impairments.
Abstract: In the past years a lot of effort has been made in the
field of face detection. The human face contains important features
that can be used by vision-based automated systems in order to
identify and recognize individuals. Face location, the primary step of
the vision-based automated systems, finds the face area in the input
image. An accurate location of the face is still a challenging task.
Viola-Jones framework has been widely used by researchers in order
to detect the location of faces and objects in a given image. Face
detection classifiers are shared by public communities, such as
OpenCV. An evaluation of these classifiers will help researchers to
choose the best classifier for their particular need. This work focuses
of the evaluation of face detection classifiers minding facial
landmarks.