Traffic-Signs Recognition System Based on FCM and Content-Based Image Retrieval

2012 ◽  
pp. 60-72
Author(s):  
Yue Li ◽  
Wei Wang
Keyword(s):  
Image Retrieval ◽  
Recognition System ◽  
Emerging Technology ◽  
Content Based Image Retrieval ◽  
Segmentation Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Artificial Intelligent ◽  
Traffic System ◽  
Two Phases

Artificial intelligent (AI) driving is an emerging technology, freeing the driver from driving. Some techniques for automatically driving have been developed; however, most can only recognize the traffic signs in particular groups, such as triangle signs for warning, circle signs for prohibition, and so forth, but cannot tell the exact meaning of every sign. In this paper, a framework for a traffic system recognition system is proposed. This system consists of two phases. The segmentation method, fuzzy c-means (FCM), is used to detect the traffic sign, whereas the Content-Based Image Retrieval (CBIR) method is used to match traffic signs to those in a database to find the exact meaning of every detected sign.

scholarly journals Detecting Change between Urban Road Environments along a Route Based on Static Road Object Occurrences

2021 ◽  
Vol 11 (8) ◽  
pp. 3666
Author(s):  
Zoltán Fazekas ◽  
László Gerencsér ◽  
Péter Gáspár
Keyword(s):  
Change Detection ◽  
Traffic Safety ◽  
Recognition System ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Safety Risk ◽  
Urban Road ◽  
Environment Type ◽  
Cusum Method

For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.

scholarly journals Content-Based Image Retrieval Based on Electromagnetism-Like Mechanism

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Hamid A. Jalab ◽  
Nor Aniza Abdullah
Keyword(s):  
Image Retrieval ◽  
Fitness Function ◽  
Optimization Technique ◽  
Content Based Image Retrieval ◽  
Retrieval Performance ◽  
Function Measurement ◽  
Average Precision ◽  
Query Image ◽  
Two Phases ◽  
Novel Algorithm

Recently, many researchers in the field of automatic content-based image retrieval have devoted a remarkable amount of research looking for methods to retrieve the best relevant images to the query image. This paper presents a novel algorithm for increasing the precision in content-based image retrieval based on electromagnetism optimization technique. The electromagnetism optimization is a nature-inspired technique that follows the collective attraction-repulsion mechanism by considering each image as an electrical charge. The algorithm is composed of two phases: fitness function measurement and electromagnetism optimization technique. It is implemented on a database with 8,000 images spread across 80 classes with 100 images in each class. Eight thousand queries are fired on the database, and the overall average precision is computed. Experimental results of the proposed approach have shown significant improvement in the retrieval performance in regard to precision.

scholarly journals New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3776 ◽  
Author(s):  
Jameel Khan ◽  
Donghoon Yeo ◽  
Hyunchul Shin
Keyword(s):  
Recognition Performance ◽  
Recognition System ◽  
Tone Mapping ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Sign Detection ◽  
Illumination Preprocessing ◽  
Traffic Sign Detection

In this paper, we propose a new Intelligent Traffic Sign Recognition (ITSR) system with illumination preprocessing capability. Our proposed Dark Area Sensitive Tone Mapping (DASTM) technique can enhance the illumination of only dark regions of an image with little impact on bright regions. We used this technique as a pre-processing module for our new traffic sign recognition system. We combined DASTM with a TS detector, an optimized version of YOLOv3 for the detection of three classes of traffic signs. We trained ITSR on a dataset of Korean traffic signs with prohibitory, mandatory, and danger classes. We achieved Mean Average Precision (MAP) value of 90.07% (previous best result was 86.61%) on challenging Korean Traffic Sign Detection (KTSD) dataset and 100% on German Traffic Sign Detection Benchmark (GTSDB). Result comparisons of ITSR with latest D-Patches, TS detector, and YOLOv3 show that our new ITSR significantly outperforms in recognition performance.

scholarly journals Real-time traffic sign detection and recognition using Raspberry Pi

2022 ◽  
Vol 12 (1) ◽  
pp. 331
Author(s):  
Ida Syafiza Binti Md Isa ◽  
Choy Ja Yeong ◽  
Nur Latif Azyze bin Mohd Shaari Azyze
Keyword(s):  
Real Time ◽  
Video Recording ◽  
Recognition System ◽  
Road Accident ◽  
Raspberry Pi ◽  
Traffic Sign ◽  
Traffic Signs ◽  
The Road ◽  
Real Time Traffic ◽  
Driving Assistance

Nowadays, the number of road accident in Malaysia is increasing expeditiously. One of the ways to reduce the number of road accident is through the development of the advanced driving assistance system (ADAS) by professional engineers. Several ADAS system has been proposed by taking into consideration the delay tolerance and the accuracy of the system itself. In this work, a traffic sign recognition system has been developed to increase the safety of the road users by installing the system inside the car for driver’s awareness. TensorFlow algorithm has been considered in this work for object recognition through machine learning due to its high accuracy. The algorithm is embedded in the Raspberry Pi 3 for processing and analysis to detect the traffic sign from the real-time video recording from Raspberry Pi camera NoIR. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 3 processor considering several scenarios related to the state of the environment and the condition of the traffic signs. A real-time testbed implementation has been conducted considering twenty different traffic signs and the results show that the system has more than 90% accuracy and is reliable with an acceptable delay.

scholarly journals Speaker Recognition in Content-based Image Retrieval for a High Degree of Accuracy

2018 ◽  
Vol 7 (3) ◽  
pp. 350-358
Author(s):  
Suhartono Suhartono ◽  
Fresy Nugroho ◽  
Muhammad Faisal ◽  
Muhammad Ainul Yaqin ◽  
Suyanta Suyanta
Keyword(s):  
Image Retrieval ◽  
Speaker Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Content Based Image Retrieval ◽  
Manhattan Distance ◽  
Test Results ◽  
Increase In Accuracy ◽  
High Degree ◽  
Support Research

The purpose of this research is to measure the speaker recognition accuracy in Content-Based Image Retrieval. To support research in speaker recognition accuracy, we use two approaches for recognition system: identification and verification, an identification using fuzzy Mamdani, a verification using Manhattan distance. The test results in this research. The best of distance mean is size 32x32. The best of the verification for distance rate is 965, and the speaker recognition system has a standard error of 5% and the system accuracy is 95%. From these results, we find that there is an increase in accuracy of almost 2.5%. This is due to a combination of two approaches so the system can add to the accuracy of speaker recognition.

scholarly journals Content-based Image Retrieval System for an Image Gallery Search Application

2018 ◽  
Vol 8 (3) ◽  
pp. 1903
Author(s):  
Nicole Tham Ley Mai ◽  
Syahmi Syahiran Bin Ahmad Ridzuan ◽  
Zaid Bin Omar
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Facial Recognition ◽  
Recognition System ◽  
Content Based Image Retrieval ◽  
Desktop Computers ◽  
Image Retrieval System ◽  
Image Query ◽  
Facial Recognition System ◽  
Search Modes

Content-based image retrieval is a process framework that applies computer vision techniques for searching and managing large image collections more efficiently. With the growth of large digital image collections triggered by rapid advances in electronic storage capacity and computing power, there is a growing need for devices and computer systems to support efficient browsing, searching, and retrieval for image collections. Hence, the aim of this project is to develop a content-based image retrieval system that can be implemented in an image gallery desktop application to allow efficient browsing through three different search modes: retrieval by image query, retrieval by facial recognition, and retrieval by text or tags. In this project, the MPEG-7-like Powered Localized Color and Edge Directivity Descriptor is used to extract the feature vectors of the image database and the facial recognition system is built around the Eigenfaces concept. A graphical user interface with the basic functionality of an image gallery application is also developed to implement the three search modes. Results show that the application is able to retrieve and display images in a collection as thumbnail previews with high retrieval accuracy and medium relevance and the computational requirements for subsequent searches were significantly reduced through the incorporation of text-based image retrieval as one of the search modes. All in all, this study introduces a simple and convenient way of offline image searches on desktop computers and provides a stepping stone to future content-based image retrieval systems built for similar purposes.

scholarly journals Real-time Multi-object Face Recognition Using Content Based Image Retrieval (CBIR)

2018 ◽  
Vol 8 (5) ◽  
pp. 2812
Author(s):  
Muhammad Fachrurrozi ◽  
Saparudin Saparudin ◽  
Erwin Erwin ◽  
Mardiana Mardiana ◽  
Clara Fin Badillah ◽  
...  
Keyword(s):  
Face Recognition ◽  
Image Retrieval ◽  
Real Time ◽  
Euclidean Distance ◽  
Time Trial ◽  
Recognition System ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Image Search ◽  
Face Recognition System

<p>Face recognition system in real time is divided into three processes, namely feature extraction, clustering, detection, and recognition. Each of these stages uses different methods, Local Binary Pattern (LBP), Agglomerative Hierarchical Clustering (AHC) and Euclidean Distance. Multi-face image search using Content Based Image Retrieval (CBIR) method. CBIR performs image search by image feature itself. Based on real time trial results, the accuracy value obtained is 61.64%. </p><p class="Abstract"> </p>

Recognition of Traffic Signs and Korean Texts on Traffic Signs Using Japanese Puzzle

2008 ◽  
Vol 12 (6) ◽  
pp. 509-515
Author(s):  
Youngsun Sohn ◽  
◽  
Ilsik Shin ◽  
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Recognition Rate ◽  
Recognition System ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Black And White ◽  
Intended Object

This paper embodies a recognition system that recognizes the traffic signs and the Korean characters on the traffic signs through reverse application of a Japanese puzzle. The Japanese puzzle used in this system reveals the shape of the intended object when marked onto the mesh grids according to the (x,y) coordinate information provided by the puzzle creator. When the puzzle described above is applied to the color and the shape of the traffic sign after the separating the traffic sign image from the inputted image, the system outputs the traffic sign and its contents as text if the image is recognized as a traffic sign. With the black-and-white image processing and unneeded penciling procedure, the proposed system outperformed the existing systems at a faster processing speed and higher recognition rate.

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