Embedded system for detection, recognition and classification of traffic signs

This study concerns the development of an embedded system with low computational resources and low power consumption. It uses the NXP LPC2106 with ARM7 processor architecture, for acquiring, processing and classifying images. This embedded system is design to detect and recognize traffic signs. Taking into account the processor capabilities and the desired features for the embedded system, a set of algorithms was developed that require low computational resources and memory. These features were accomplished using a modified Freeman Method in conjunction with a new algorithm "ear pull" proposed in this work. Each of these algorithms was tested with static images, using code developed for MATLAB and for the CMUcam3. The road environment was simulated and experimental tests were performed to measure traffic signs recognition rate on real environment. The technical limitations imposed by the embedded system led to an increased complexity of the project, however the final results provide a recognition rate of 77% on road tests.Thus, the embedded system features overcome the initial expectations and highlight the potentialities of both algorithms that were developed.info:eu-repo/semantics/publishedVersio

Stereoscopic vision in vehicle navigation.

Traffic sign (TS) detection and tracking is one of the main tasks of an autonomous vehicle which is addressed in the field of computer vision. An autonomous vehicle must have vision based recognition of the road to follow the rules like every other vehicle on the road. Besides, TS detection and tracking can be used to give feedbacks to the driver. This can significantly increase safety in making driving decisions. For a successful TS detection and tracking changes in weather and lighting conditions should be considered. Also, the camera is in motion, which results in image distortion and motion blur. In this work a fast and robust method is proposed for tracking the stop signs in videos taken with stereoscopic cameras that are mounted on the car. Using camera parameters and the detected sign, the distance between the stop sign and the vehicle is calculated. This calculated distance can be widely used in building visual driver-assistance systems

Fast and Robust Traffic Sign Detection

This paper deals with the fast and robust detection of the traffic sign images. A new technique called geometric fragmentation is proposed to detect the red circular traffic signs. It detects the outer ellipses of the signs by combining the left and right fragments of the ellipse objects. A search based on the geometric fragmentation is used to find the ellipse fragments. This search is somewhat similar to genetic algorithm (GA) in the sense that it employs the terms of individual, population, crossover, and objective function usually used in GA. To increase the accuracy and reduce the computational time, a new objective function is introduced for evaluating the individuals. The algorithm was tested for detecting the red circular traffic signs from the real scene image. The experimental results show that the proposed algorithm has a higher detection rate with a lower computational cost compared with the referential genetic algorithm-based ellipse detection

Assessment of Driver\u27s Attention to Traffic Signs through Analysis of Gaze and Driving Sequences

A driver’s behavior is one of the most significant factors in Advance Driver Assistance Systems. One area that has received little study is just how observant drivers are in seeing and recognizing traffic signs. In this contribution, we present a system considering the location where a driver is looking (points of gaze) as a factor to determine that whether the driver has seen a sign. Our system detects and classifies traffic signs inside the driver’s attentional visual field to identify whether the driver has seen the traffic signs or not. Based on the results obtained from this stage which provides quantitative information, our system is able to determine how observant of traffic signs that drivers are. We take advantage of the combination of Maximally Stable Extremal Regions algorithm and Color information in addition to a binary linear Support Vector Machine classifier and Histogram of Oriented Gradients as features detector for detection. In classification stage, we use a multi class Support Vector Machine for classifier also Histogram of Oriented Gradients for features. In addition to the detection and recognition of traffic signs, our system is capable of determining if the sign is inside the attentional visual field of the drivers. It means the driver has kept his gaze on traffic signs and sees the sign, while if the sign is not inside this area, the driver did not look at the sign and sign has been missed

Application of Biometric Components for Personal Identification in Electromobility Area

Import 22/07/2015Bakalářská práce řeší problematiku biometrické identifikace a verifikace osob. Zaměřuje se na rozpoznávání pomocí otisku prstu. Rozpoznávání je řešeno v oblasti elektromobility. Cílem práce je najít vhodný senzor otisku prstu pro vytvoření hardwaru. Pro tento senzor dále navrhnout a vytvořit program vhodný pro identifikaci a verifikaci osob. Po průzkumu trhu byl nalezen vhodný senzor. Pomoci převodníku je spojen s počítačem. Následuje vývoj programu pro rozpoznávání a identifikaci osob. Poté bude doplněn o databázi údajů uživatelů.This thesis solves the issue of biometric identification and verification of people. The thesis focuses on Fingerprint recognition. The recognition is solved in Electromobility Area. The goal of this thesis is to discover a suitable fingerprint sensor for using in the hardware part. For fingerprint sensor will be designed and created an appropriate program for identification and verification of persons. We was found the suitable fingerprint sensor, after the market research. Sensor is connected to the computer with other component. This part of thesis is followed by the development of the program for verification and identification. Then it will be complemented by a database of data users.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Affine invariant Matching Pursuit-based shape representation and recognition using scale-space

In this paper, we propose an analytical low-level representation of images, obtained by a decomposition process, here the matching pursuit (MP) algorithm, as a new way of describing objects through a general continuous description using an affine invariant dictionary of basis functions. This description is used to recognize objects in images. In the learning phase, a template object is decomposed, and the extracted subset of basis functions, called meta-atom, gives the description of our object. We then extend naturally this description into the linear scale-space using the definition of our basis functions, and thus bringing a more general representation of our object. We use this enhanced description as a predefined dictionary of the object to conduct an MP-based shape recognition (MPSR) task into the linear scale-space. The introduction of the scale-space approach improves the robustness of our method, and permits to avoid local minima problems encountered when minimizing a non-convex energy function. We show results for the detection of complex synthetic shapes, as well as natural (aerial and medical) images

An optimization on pictogram identification for the road-sign recognition task using svms

Pattern recognition methods are used in the final stage of a traffic sign detection and recognition system, where the main objective is to categorize a detected sign. Support vector machines have been reported as a good method to achieve this main target due to their ability to provide good accuracy as well as being sparse methods. Nevertheless, for complete data sets of traffic signs the number of operations needed in the test phase is still large, whereas the accuracy needs to be improved. The objectives of this work are to propose pre-processing methods and improvements in support vector machines to increase the accuracy achieved while the number of support vectors, and thus the number of operations needed in the test phase, is reduced. Results show that with the proposed methods the accuracy is increased 3?5% with a reduction in the number of support vectors of 50?70%