Recognition and 3D Localization of Traffic Signs via Image-Based Point Cloud Models

Abstract

Recently, the US Departments of Transportation have pro-actively looked into videotaping roadway assets. Using inspection vehicles equipped with high resolution cameras, accurate information on location and condition of high quantity and low cost roadway assets are being collected. While many efforts have focused on streamlining the data collection, the analysis is still manual and involves painstaking and subjective processes. Their high cost has also limited the scope of the visual assessments to critical roadways only. To address current limitations, this paper presents an automated method to detect, classify, and accurately localize traffic signs in 3D using existing visual data. Using a discriminative learning method based on Histograms of Oriented Gradients and Color, traffic signs are detected and classified into multiple categories. Then, a Structure from Motion procedure creates a 3D point cloud from the street level images, and triangulates the location of the detected signs in 3D. The experimental results show that the method reliably detects and localizes traffic signs and demonstrate a strong potential in improving assessments and lowering cost in practical applications.

Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data

“…Table 7 summarizes the overall classification performance obtained by three classifiers measured with precision, recall and F1 score using Equation (15). Table 8 presents the differences in classification performance between: (1) SVM and short-range CRF; (2) SVM and multi-range CRF; and (3) short-range CRF and long-range CRF.…”

“…They reported that the highway assets can be extracted from segmented point cloud, which reached 86.75% average per-pixel accuracy. They continued their work [15] on applying SVM to classify a range of traffic signs from the 3D point cloud generated by Structure from Motion (SfM). However, the main limitation of local classifiers is that they do not consider neighbor relations, causing ambiguities of features among classes.…”

Detection, classification, and mapping of U.S. traffic signs using google street view images for roadway inventory management

“…Rather the most appropriate approach depends on an agency's needs and culture as well as the availability of economic, technological, and human resources. (Balali and Golparvar-Fard 2015b;de la Garza et al 2010;Haas and Hensing 2005;Jalayer et al 2013) have shown that the utility of a particular inventory technique depends on the type of features to be collected such as location, sign type, spatial measurement, and material property visual measurement. As shown in Table 2, in all these cases the data is still collected and analyzed manually and thus inventory databases cannot be quickly or frequently updated.…”

A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation

“…Intensity has been a widely utilized cue in the segmentation of road markings and road signs [8][9][10][34][35][36][37][38][39][40][41]. Intensity values are crucial in these methods because road markings are usually made of special pavement marking material and have higher reflective ability than does the remainder of the road surface.…”