scholarly journals FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B3 ◽  
pp. 937-944
Author(s):  
Q. Yao ◽  
B. Tan ◽  
Y. Huang
Keyword(s):  
Asset Management ◽  
Large Scale ◽  
Rare Event ◽  
Drawing Process ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Anchor Points

Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.

scholarly journals FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B3 ◽  
pp. 937-944
Author(s):  
Q. Yao ◽  
B. Tan ◽  
Y. Huang
Keyword(s):  
Asset Management ◽  
Large Scale ◽  
Rare Event ◽  
Drawing Process ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Anchor Points

Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.

scholarly journals LIDAR-INCORPORATED TRAFFIC SIGN DETECTION FROM VIDEO LOG IMAGES OF MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B1 ◽  
pp. 661-668 ◽  
Author(s):  
Y. Li ◽  
J. Fan ◽  
Y. Huang ◽  
Z. Chen
Keyword(s):  
Geometric Constraints ◽  
Transportation Infrastructure ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Sign Detection ◽  
Mobile Mapping System ◽  
Transportation Agencies ◽  
Traffic Sign Detection

Mobile Mapping System (MMS) simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs) of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the rate of detecting traffic signs with the incorporation of the 3D planar constraint of their Lidar points. It is promising for the robust and large-scale survey of most transportation infrastructure with the application of MMS.

scholarly journals Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

2020 ◽  
Vol 12 (3) ◽  
pp. 442 ◽  
Author(s):  
Jesús Balado ◽  
Elena González ◽  
Pedro Arias ◽  
David Castro
Keyword(s):  
False Positive Rate ◽  
Good Condition ◽  
Point Clouds ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Novel Approach ◽  
Mapping System ◽  
Positive Rate ◽  
Mobile Mapping System

Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection–classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.

scholarly journals LIDAR-INCORPORATED TRAFFIC SIGN DETECTION FROM VIDEO LOG IMAGES OF MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B1 ◽  
pp. 661-668
Author(s):  
Y. Li ◽  
J. Fan ◽  
Y. Huang ◽  
Z. Chen
Keyword(s):  
Geometric Constraints ◽  
Transportation Infrastructure ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Sign Detection ◽  
Mobile Mapping System ◽  
Transportation Agencies ◽  
Traffic Sign Detection

Mobile Mapping System (MMS) simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs) of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the rate of detecting traffic signs with the incorporation of the 3D planar constraint of their Lidar points. It is promising for the robust and large-scale survey of most transportation infrastructure with the application of MMS.

scholarly journals Automatic 3D City Modeling Using a Digital Map and Panoramic Images from a Mobile Mapping System

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hyungki Kim ◽  
Yuna Kang ◽  
Soonhung Han
Keyword(s):  
Virtual Reality ◽  
Visual Information ◽  
Large Scale ◽  
Mobile Mapping ◽  
3D Navigation ◽  
Digital Map ◽  
Mapping System ◽  
City Modeling ◽  
Mobile Mapping System ◽  
City Models

Three-dimensional city models are becoming a valuable resource because of their close geospatial, geometrical, and visual relationship with the physical world. However, ground-oriented applications in virtual reality, 3D navigation, and civil engineering require a novel modeling approach, because the existing large-scale 3D city modeling methods do not provide rich visual information at ground level. This paper proposes a new framework for generating 3D city models that satisfy both the visual and the physical requirements for ground-oriented virtual reality applications. To ensure its usability, the framework must be cost-effective and allow for automated creation. To achieve these goals, we leverage a mobile mapping system that automatically gathers high-resolution images and supplements sensor information such as the position and direction of the captured images. To resolve problems stemming from sensor noise and occlusions, we develop a fusion technique to incorporate digital map data. This paper describes the major processes of the overall framework and the proposed techniques for each step and presents experimental results from a comparison with an existing 3D city model.

Dynamic Maintenance Data Mining of Traffic Sign Based on Mobile Mapping System

2013 ◽  
Vol 455 ◽  
pp. 438-441 ◽  
Author(s):  
Dan Li ◽  
Wen Ying Su
Keyword(s):  
Information System ◽  
Intelligent Transportation System ◽  
Traffic Information ◽  
Dynamic Traffic ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Rapid Acquisition ◽  
Mapping System ◽  
Mobile Mapping System ◽  
A New Technique

A dynamic traffic information system is an essential component of an intelligent transportation system. Mobile mapping system (MMS) is a new technique that can be used to improve capability of the information gathering. Article describes the measurement system based on mobile mapping system increased relevant software and hardware that can be used to collect maintenance information of traffic sign. Build a dynamic traffic sign maintenance information system with the function of rapid acquisition and analysis, these improve the accuracy and immediacy of traffic sign maintenance information processing.

scholarly journals LARGE SCALE TEXTURED MESH RECONSTRUCTION FROM MOBILE MAPPING IMAGES AND LIDAR SCANS

2018 ◽  
Vol IV-2 ◽  
pp. 49-56 ◽  
Author(s):  
M. Boussaha ◽  
B. Vallet ◽  
P. Rives
Keyword(s):  
Large Scale ◽  
Texture Mapping ◽  
Time Consistency ◽  
High Quality ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Fully Automatic ◽  
Reconstructed Surface ◽  
Mesh Reconstruction

The representation of 3D geometric and photometric information of the real world is one of the most challenging and extensively studied research topics in the photogrammetry and robotics communities. In this paper, we present a fully automatic framework for 3D high quality large scale urban texture mapping using oriented images and LiDAR scans acquired by a terrestrial Mobile Mapping System (MMS). First, the acquired points and images are sliced into temporal chunks ensuring a reasonable size and time consistency between geometry (points) and photometry (images). Then, a simple, fast and scalable 3D surface reconstruction relying on the sensor space topology is performed on each chunk after an isotropic sampling of the point cloud obtained from the raw LiDAR scans. Finally, the algorithm proposed in (Waechter et al., 2014) is adapted to texture the reconstructed surface with the images acquired simultaneously, ensuring a high quality texture with no seams and global color adjustment. We evaluate our full pipeline on a dataset of 17 km of acquisition in Rouen, France resulting in nearly 2 billion points and 40000 full HD images. We are able to reconstruct and texture the whole acquisition in less than 30 computing hours, the entire process being highly parallel as each chunk can be processed independently in a separate thread or computer.

scholarly journals MOBILE MAPPING SYSTEM BASED ON ACTION CAMERAS

2018 ◽  
Vol XLII-1 ◽  
pp. 167-171
Author(s):  
J. A. Gonçalves ◽  
A. Pinhal
Keyword(s):  
Large Scale ◽  
Tall Buildings ◽  
Dual Frequency ◽  
Mobile Mapping ◽  
Video Frames ◽  
Mapping System ◽  
Differential Positioning ◽  
Mobile Mapping System ◽  
Vehicle Trajectory ◽  
Processing Techniques

<p><strong>Abstract.</strong> Action cameras can operate in outdoor conditions, such as outside a car, and provide good quality imagery that can be exploited to collect geospatial data by photogrammetric means. Recent models include GPS, which can deliver position and time of individual images and video frames. That is the case of the very popular camera, Gopro Hero 5. This paper describes the implementation of a mobile mapping system, based on a GoPro Hero 5 camera mounted on the side rearview mirror of a car. Although the system can be dependent on the camera GPS positions only, it was developed to include a GNSS dual frequency receiver, carried inside the car, on the dashboard. Within good observation conditions, without tall buildings, differential positioning (either RTK or PPK) provides the trajectory with accuracy of a few centimetres. The precise time of individual frames is obtained from the camera GPS and positions are interpolated from the GNSS receiver. Assuming the car moves in a horizontal plane and the camera has no significant tilts, the system is treated in planimetric terms, with camera axis azimuth derived from the vehicle trajectory. Positions of observed objects, such as traffic signs, are derived from consecutive frames. Tests carried out in a sparse urban environment have shown planimetric accuracy better than 40<span class="thinspace"></span>cm, appropriate for large scale mapping, such as 1<span class="thinspace"></span>:<span class="thinspace"></span>2000. The system can be improved in several forms, through processing techniques, such as structure from motion, but without the incorporation of additional hardware.</p>

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