A Camera and LiDAR Data Fusion Method for Railway Object Detection

Wang, Zhangyu; Yu, Guizhen; Wu, Xinkai; Li, Haoran; Li, Da

doi:10.1109/jsen.2021.3066714

Cited by 52 publications

(25 citation statements)

References 33 publications

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“…Xu et al developed a seamless indoor pedestrian tracking scheme using least square-SVM (LS-SVM) assisted unbiased finite impulse response filter to achieve seamless reliable human position monitoring in indoor environments [13]. Wang et al proposed a multi-sensor framework to fuse camera and LiDAR data to detect objects on a railway track, including small obstacles and forward trains [14]. Stadler et al proposed an occlusion handling strategy that explicitly models the relation between occluding and occluded tracks to achieve reliable pedestrian re-identification when occlusion occurs [15].…”

Section: Related Workmentioning

confidence: 99%

“…3 compares some existing studies with our framework. Although the referenced studies [4][5][6]13,14] have proposed to use various information sources to track targets, they have not tried to use pedestrian attribute information for tracking. As presented in paper [16], although there is research on pedestrian attributes, its purpose is to obtain pedestrian attributes, not to use pedestrian attributes.…”

Section: Noveltymentioning

confidence: 99%

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Tracking Pedestrians Under Occlusion in Parking Space

Zhou¹,

Yamada²,

Watanabe³

et al. 2023

Computer Systems Science and Engineering

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Many traffic accidents occur in parking lots. One of the serious safety risks is vehicle-pedestrian conflict. Moreover, with the increasing development of automatic driving and parking technology, parking safety has received significant attention from vehicle safety analysts. However, pedestrian protection in parking lots still faces many challenges. For example, the physical structure of a parking lot may be complex, and dead corners would occur when the vehicle density is high. These lead to pedestrians' sudden appearance in the vehicle's path from an unexpected position, resulting in collision accidents in the parking lot. We advocate that besides vehicular sensing data, high-precision digital map of the parking lot, pedestrians' smart device's sensing data, and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot. However, this subject has not been studied and explored in existing studies. To fill this void, this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces. We also evaluate the proposed method through real-world experiments. The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy. It can also be used for pedestrian tracking in parking spaces.

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Section: Related Workmentioning

confidence: 99%

Section: Noveltymentioning

confidence: 99%

Tracking Pedestrians Under Occlusion in Parking Space

Zhou¹,

Yamada²,

Watanabe³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Ristić-Durrant et al [37][38][39][40] have developed an advanced sensor setup, by combining a multi-baseline stereo camera setup with a thermal camera and a dense LiDAR scanner to detect objects on the tracks at ranges approaching the 1000 m mark. Fusing a simple camera and a LiDAR scanner [41][42][43][44][45] has shown promising results at ranges between 50 m and 300 m, depending on object sizes.…”

Section: Railway Obstacle Detectionmentioning

confidence: 99%

High-Precision Low-Cost Gimballing Platform for Long-Range Railway Obstacle Detection

Assaf

Cornelius

Cadena

et al. 2022

Sensors

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Increasing demand for rail transportation results transportation by rail, resulting in denser and more high-speed usage of the existing railway network, making makes new and more advanced vehicle safety systems necessary. Furthermore, high traveling speeds and the greatlarge weights of trains lead to long braking distances—all of which necessitates Long braking distances, due to high travelling speeds and the massive weight of trains, necessitate a Long-Range Obstacle Detection (LROD) system, capable of detecting humans and other objects more than 1000 m in advance. According to current research, only a few sensor modalities are capable of reaching this far and recording sufficiently accurate enoughdata to distinguish individual objects. The limitation of these sensors, such as a 1D-Light Detection and Ranging (LiDAR), is however a very narrow Field of View (FoV), making it necessary to use ahigh-precision means of orienting to target them at possible areas of interest. To close this research gap, this paper presents a novel approach to detecting railway obstacles by developinga high-precision pointing mechanism, for the use in a future novel railway obstacle detection system In this work such a high-precision pointing mechanism is developed, capable of targeting aiming a 1D-LiDAR at humans or objects at the required distance. This approach addresses To address the challenges of a low target pricelimited budget, restricted access to high-precision machinery and equipment as well as unique requirements of our target application., a novel pointing mechanism has been designed and developed. By combining established elements from 3D printers and Computer Numerical Control (CNC) machines with a double-hinged lever system, simple and cheaplow-cost components are capable of precisely orienting an arbitrary sensor platform. The system’s actual pointing accuracy has been evaluated using a controlled, in-door, long-range experiment. The device was able to demonstrate a precision of 6.179 mdeg, which is at the limit of the measurable precision of the designed experiment.

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“…To identify and quantify diverse surface defects, Erkal et al [ 121 ] developed a surface property monitoring method using LiDAR vision, which is primarily used to provide color data as fourth dimension information. In terms of subway safety monitoring, Zhangyu et al [ 122 ] fused camera and LiDAR data to detect small obstacles and vehicles. Vision technology achieved pixel-level ROI area separation, and LiDAR data were used to estimate the distance of vehicles ahead and detect small obstacles.…”

Section: Vision–laser-based Infrastructure Monitoringmentioning

confidence: 99%

“…Vision Range Laser [95][96][97] Total station-based deformation measurement [98] Low-temperature environment deformation monitoring [99] Railway crack detection Structured Light Vision [100] Point laser structured light [101][102][103] Texture surface monitoring [104] Railway tunnels monitoring LiDAR Vision [111][112][113][114] LiDAR camera calibration [115] Infrastructure deformation [116,117] Crack monitoring [118,119] Pavement pit monitoring [120,121] Surface defects monitoring with color information [122] Subway obstacles and vehicles [123] Large structures monitoring [124][125][126] UAV with LiDAR and cameras [127][128][129] Post-earthquake and urban area monitoring…”

Section: Ref Monitoring Typesmentioning

confidence: 99%

A Review of Vision-Laser-Based Civil Infrastructure Inspection and Monitoring

Zhou

Tang

et al. 2022

Sensors

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Structural health and construction security are important problems in civil engineering. Regular infrastructure inspection and monitoring methods are mostly performed manually. Early automatic structural health monitoring techniques were mostly based on contact sensors, which usually are difficult to maintain in complex infrastructure environments. Therefore, non-contact infrastructure inspection and monitoring techniques received increasing interest in recent years, and they are widely used in all aspects of infrastructure life, owing to their convenience and non-destructive properties. This paper provides an overview of vision-based inspection and vision–laser-based monitoring techniques and applications. The inspection part includes image-processing algorithms, object detection, and semantic segmentation. In particular, infrastructure monitoring involves not only visual technologies but also different fusion methods of vision and lasers. Furthermore, the most important challenges for future automatic non-contact inspections and monitoring are discussed and the paper correspondingly concludes with state-of-the-art algorithms and applications to resolve these challenges.

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A Camera and LiDAR Data Fusion Method for Railway Object Detection

Cited by 52 publications

References 33 publications

Tracking Pedestrians Under Occlusion in Parking Space

Tracking Pedestrians Under Occlusion in Parking Space

High-Precision Low-Cost Gimballing Platform for Long-Range Railway Obstacle Detection

A Review of Vision-Laser-Based Civil Infrastructure Inspection and Monitoring

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