Colour vision model-based approach for segmentation of traffic signs

This paper presents a new approach to segment traffic signs from the rest of a scene via CIECAM, a colour appearance model. This approach not only takes CIECAM into practical application for the first time since it was standardised in 1998, but also introduces a new way of segmenting traffic signs in order to improve the accuracy of colour-based approach. Comparison with the other CIE spaces, including CIELUV and CIELAB, and RGB colour space is also carried out. The results show that CIECAM performs better than the other three spaces with 94%, 90%, and 85% accurate rates for sunny, cloudy, and rainy days, respectively. The results also confirm that CIECAM does predict the colour appearance similar to average observers

Traffic sign recognition based on human visual perception.

This thesis presents a new approach, based on human visual perception, for detecting and recognising traffic signs under different viewing conditions. Traffic sign recognition is an important issue within any driver support system as it is fundamental to traffic safety and increases the drivers' awareness of situations and possible decisions that are ahead. All traffic signs possess similar visual characteristics, they are often the same size, shape and colour. However shapes may be distorted when viewed from different viewing angles and colours are affected by overall luminosity and the presence of shadows. Human vision can identify traffic signs correctly by ignoring this variance of colours and shapes. Consequently traffic sign recognition based on human visual perception has been researched during this project. In this approach two human vision models are adopted to solve the problems above: Colour Appearance Model (CIECAM97s) and Behavioural Model of Vision (BMV). Colour Appearance Model (CIECAM97s) is used to segment potential traffic signs from the image background under different weather conditions. Behavioural Model of Vision (BMV) is used to recognize the potential traffic signs. Results show that segmentation based on CIECAM97s performs better than, or comparable to, other perceptual colour spaces in terms of accuracy. In addition, results illustrate that recognition based on BMV can be used in this project effectively to detect a certain range of shape transformations. Furthermore, a fast method of distinguishing and recognizing the different weather conditions within images has been developed. The results show that 84% recognition rate can be achieved under three weather and different viewing conditions

Traffic sign recognition based on human visual perception

This thesis presents a new approach, based on human visual perception, for detecting and recognising traffic signs under different viewing conditions. Traffic sign recognition is an important issue within any driver support system as it is fundamental to traffic safety and increases the drivers' awareness of situations and possible decisions that are ahead. All traffic signs possess similar visual characteristics, they are often the same size, shape and colour. However shapes may be distorted when viewed from different viewing angles and colours are affected by overall luminosity and the presence of shadows. Human vision can identify traffic signs correctly by ignoring this variance of colours and shapes. Consequently traffic sign recognition based on human visual perception has been researched during this project. In this approach two human vision models are adopted to solve the problems above: Colour Appearance Model (CIECAM97s) and Behavioural Model of Vision (BMV). Colour Appearance Model (CIECAM97s) is used to segment potential traffic signs from the image background under different weather conditions. Behavioural Model of Vision (BMV) is used to recognize the potential traffic signs. Results show that segmentation based on CIECAM97s performs better than, or comparable to, other perceptual colour spaces in terms of accuracy. In addition, results illustrate that recognition based on BMV can be used in this project effectively to detect a certain range of shape transformations. Furthermore, a fast method of distinguishing and recognizing the different weather conditions within images has been developed. The results show that 84% recognition rate can be achieved under three weather and different viewing conditions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Fast traffic sign recognition using color segmentation and deep convolutional networks

The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelli- gent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Ori- ented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a pre- processing step to reduce the search space, while classication is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traf- c Sign data set and on the novel Data set of Italian Trac Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the eectiveness of the proposed ap- proach in terms of both classication accuracy and computational speed

Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

Object detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic signs as objects play a vital role in guiding such systems. However, if the vehicle fails to locate any critical sign, it might make a catastrophic failure. In this paper, we propose an approach to identify traffic signs that have been mistakenly discarded by the object detector. The proposed method raises an alarm when it discovers a failure by the object detector to detect a traffic sign. This approach can be useful to evaluate the performance of the detector during the deployment phase. We trained a single shot multi-box object detector to detect traffic signs and used its internal features to train a separate false negative detector (FND). During deployment, FND decides whether the traffic sign detector (TSD) has missed a sign or not. We are using precision and recall to measure the accuracy of FND in two different datasets. For 80% recall, FND has achieved 89.9% precision in Belgium Traffic Sign Detection dataset and 90.8% precision in German Traffic Sign Recognition Benchmark dataset respectively. To the best of our knowledge, our method is the first to tackle this critical aspect of false negative detection in robotic vision. Such a fail-safe mechanism for object detection can improve the engagement of robotic vision systems in our daily life.Comment: Submitted to the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019

A disaster risk assessment model for the conservation of cultural heritage sites in Melaka Malaysia

There exist ongoing efforts to reduce the exposure of Cultural Heritage Sites (CHSs) to Disaster Risk (DR). However, a complicated issue these efforts face is that of ‘estimation’ whereby no standardised unit exist for assessing the effects of Cultural Heritage (CH) exposed to DR as compared to other exposed items having standardised assessment units such as; ‘number of people’ for deaths, injured and displaced, ‘dollar’ for economic impact, ‘number of units’ for building stock or animals among others. This issue inhibits the effective assessment of CHSs exposed to DR. Although there exist several DR assessment frameworks for conserving CHSs, the conceptualisation of DR in these studies fall short of good practice such as international strategy for disaster reduction by United Nations which expresses DR to being a hollistic interplay of three variables (hazard, vulnerability and capacity). Adopting such good practice, this research seeks to propose a mechanism of DR assessment aimed at reducing the exposure of CHSs to DR. Quantitative method adopted for data collection involved a survey of 365 respondents at CHSs in Melaka using a structured questionnaire. Similarly, data analysis consisted of a two-step Structural Equation Modelling (measurement and structural modelling). The achievement of the recommended thresholds for unidimensionality, validity and reliability by the measurement models is a testimony to the model fitness for all 8 first-order independent variables and 2 first-order dependent variables. While hazard had a ‘small’ but negative effect, vulnerability had a ‘very large’ but negative effect on the exposure of CHSs to DR. Likewise, capacity had a ‘small’ but positive effect on the exposure of CHSs to DR. The outcome of this study is a Disaster Risk Assessment Model (DRAM) aimed at reducing DR to CHSs. The implication of this research is providing insights on decisions for DR assessment to institutions, policymakers and statutory bodies towards their approach to enhancing the conservation of CHSs