Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

Real-Time Recognition System for Traffic Signs

The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. Inreal time environment, vehicles move at high speed on roads. For the vehicle intelligent system itbecomes essential to detect, process and recognize the traffic sign which is coming in front ofvehicle with high relative velocity, at the right time, so that the driver would be able to pro-actsimultaneously on instructions given in the Traffic Sign. The system assists drivers about trafficsigns they did not recognize before passing them. With the Traffic Sign Recognition system, thevehicle becomes aware of the traffic environment and reacts according to the situation.The objective of the project is to develop a system which can recognize the traffic signs in real time.The three target parameters are the system’s response time in real-time video streaming, the trafficsign recognition speed in still images and the recognition accuracy. The system consists of threeprocesses; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. Thedetection process uses physical properties of traffic signs based on a priori knowledge to detect roadsigns. It generates the road sign image as the input to the recognition process. The recognitionprocess is implemented using the Pattern Matching algorithm. The system was first tested onstationary images where it showed on average 97% accuracy with the average processing time of0.15 seconds for traffic sign recognition. This procedure was then applied to the real time videostreaming. Finally the tracking of traffic signs was developed using Blob tracking which showed theaverage recognition accuracy to 95% in real time and improved the system’s average response timeto 0.04 seconds. This project has been implemented in C-language using the Open Computer VisionLibrary.

An Upgrade of Network Traffic Recognition System for SIP/VoIP Traffic Recognition

The purpose of this project is to update the tool of Network Traffic Recognition System (NTRS) which is proprietary software of Ericsson AB and Tsinghua University, and to implement the updated tool to finish SIP/VoIP traffic recognition. Basing on the original NTRS, I analyze the traffic recognition principal of NTRS, and redesign the structure and module of the tool according to characteristics of SIP/VoIP traffic, and then finally I program to achieve the upgrade. After the final test with our SIP data trace files in the updated system, a satisfactory result is derived. The result presents that our updated system holds a rate of recognition on a confident level in the SIP session recognition as well as the VoIP call recognition. In the comparison with the software of Wireshark, our updated system has a result which is extremely close to Wireshark’s output, and the working time is much less than Wireshark. In the aspect of practicability, the memory overflow problem is avoided, and the updated system can output the specific information of SIP/VoIP traffic recognition, such as SIP type, SIP state, VoIP state, etc. The upgrade fulfills the demand of this project.

Racial Conflict in the United States of America : A Deconstructive Perspective on Native Speaker by Changrae Lee

Written about the time of the Golden Venture incident, Chang-rae Lee’s Native Speaker makes a particular reference to that incident, whereby implying that particular immigrants, on the grounds of their racial identities, are mistreated and considered as aliens by some Americas. While some whites discriminate against immigrants, there is widespread ethnic tension between Korean Americans and African Americans. Significantly, racial conflict between Koreans and blacks and the racist attitude of some whites toward immigrants are mirrored in the relationship between the Korean-American protagonist Henry and his American wife Lelia. That is, due to their different racial identities they do not understand each other and they always argue. However, toward the end of the novel, Henry and Lelia come to understand each other. While ethnic conflict between Koreans and blacks and certain whites’ discriminatory attitudes toward immigrants is serious one, the novel suggests the unimportance of racial identity. In other words, the novel concludes that there is no discriminatory treatment of immigrants and, in fact, every one is a native Speaker in America. In the novel there is no message of how racial conflict could be resolved. However, this essay suggests that by investigating how the tension between Henry and Lelia is resolved, one could suggest a solution for the ethnicity problem in America and in real life.

Hand Gesture Detection & Recognition System

The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures.Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.

The latitude dependent irradiation distribution in Europe and its implication for the design of stationary concentrators

When a stationary solar concentrator is designed, the spatial distribution of the available irradiation is of vital interest. An irradiation distribution based only on solar geometry will look similar at different sites. The only difference is that the distribution of the incident irradiation is shifted to lower solar altitudes when latitude is increased. However, real irradiation distribution will show strong asymmetry at high latitude sites, since the winter irradiation is reduced by absorption and scattering in the atmosphere, and by seasonal changes in the climate. The reduced winter irradiation at high latitudes implies that the available annual radiation is concentrated to a narrower angular interval. This means that the degree of concentration that is possible increases with latitude.In the paper examples of irradiation distribution from different sites in Europe from latitude 38°N to 65°N are shown. The origin of the reduced winter irradiation with increased latitude is discussed, and numerical examples on the performance of different types of stationary concentrators for different latitudes are given.

”Jag tror på något” : Konstruktioner av tro och tilltro

”I believe in something”: Constructions of Beliefs in Sweden Research results are dependent on how social phenomena are conceptualized. In the present article, consequences are discussed of the standardized conceptualization of religiosity as ”church-oriented”. In its place, a multifaceted approach to the phenomenon of religious belief is suggested. This approach was used for an analysis on how eleven Swedish women and men with different religious and spiritual as well as non-religious and atheist affiliation talk about beliefs. The results suggest that beliefs were meaningful because they related to specific perceptions of a Zeitgeist. It was hereafter underlined that belief in ”something” or other brief descriptions of the sacred placed the sacred outside of the individual. Finally, while subjective authority is valued for choosing to believe, this subjectivity seems in part to be dependent on collective dimensions of recognition. The value of choosing beliefs can be conceptualized as a meaningful yet analytically distinct aspect of belief co-existing with descriptions of the sacred. Thus, it is concluded that a multifaceted approach to religious belief may develop our understanding of religion and religiosity in contemporary society

Motion Cues Analysis for Parkinson Gait Recognition

Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.

What affects recognition most – wrong wordstress or wrong word accent?

In an attempt to find out which of the two Swedish prosodic contrasts of 1) wordstress pattern and 2) tonal word accent category has the greatest communicative weight, a lexical decision experiment was conducted: in one part word stress pattern was changed from trochaic to iambic, and in the other part trochaic accentII words were changed to accent I.Native Swedish listeners were asked to decide whether the distorted words werereal words or ‘non-words’. A clear tendency is that listeners preferred to give more‘non-word’ responses when the stress pattern was shifted, compared to when wordaccent category was shifted. This could have implications for priority of phonological features when teaching Swedish as a second language.

Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

Motion cue analysis for parkinsonian gait recognition

This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson's disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject's body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

Opening ILT blackbox: Exploring recognition-based leadership perceptions with conjoint analysis

Although research on Implicit Leadership Theories (ILT) has put great effort on determining what attributes define a leader prototype, little attention has been given to understanding the relative importance of each of these attributes in the categorization process by followers. Knowing that recognition-based leadership perceptions are the result of the match between followers’ ILTs and the perceived attributes in their actual leaders, understanding how specific prototypical leader attributes impact this impression formation process is particularly relevant. In this study, we draw upon socio-cognitive theories to explore how followers cognitively process the information about a leader’s attributes. By using Conjoint Analysis (CA), a technique that allows us to measure an individual’s trade-offs when making choices about multi-attributed options, we conducted a series of 4 studies with a total of 879 participants. Our results demonstrate that attributes’ importance for individuals’ leadership perceptions formation is rather heterogeneous, and that some attributes can enhance or spoil the importance of other prototypical attributes. Finally, by manipulating the leadership domain, we show that the weighting pattern of attributes is context dependent, as suggested by the connectionist approach to leadership categorization. Our findings also demonstrate that Conjoint Analysis can be a valuable tool for ILT research.