Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data

Rateke, Thiago; Wangenheim, Aldo von

doi:10.1007/s00138-020-01126-w

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…As the optical flow concept is a major aspect of a human (animal) vision, the technique is also adopted for development of machine vision [28]. The optical flow-based techniques are intensively developed and used for a wide spectrum of different purposes like fault detection in production processes [29], intelligent vehicle navigation [30], micro air vehicles control [31], structural displacement monitoring [32] and many more (see [33][34][35]).…”

Section: Introductionmentioning

confidence: 99%

Optical flow method for measuring deformation of soil specimen subjected to torsional shearing

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Optical flow method for measuring deformation of soil specimen subjected to torsional shearing

et al. 2021

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“…However, this paper only tells the process of roadside units to identify obstacles at the broad level and does not consider the situation when the road appears as a broad obstacle. Rateke et al [ 3 ] used convolutional neural networks for obstacle detection. The CNN (Convolutional Neural Networks)-based detection and target recognition results were combined with the depth pattern of the parallax map and the motion pattern of the optical flow in the validation phase.…”

Section: Related Workmentioning

confidence: 99%

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Ding

Zhang³

et al. 2023

Sensors

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Road obstacle detection is an important component of intelligent assisted driving technology. Existing obstacle detection methods ignore the important direction of generalized obstacle detection. This paper proposes an obstacle detection method based on the fusion of roadside units and vehicle mounted cameras and illustrates the feasibility of a combined monocular camera inertial measurement unit (IMU) and roadside unit (RSU) detection method. A generalized obstacle detection method based on vision IMU is combined with a roadside unit obstacle detection method based on a background difference method to achieve generalized obstacle classification while reducing the spatial complexity of the detection area. In the generalized obstacle recognition stage, a VIDAR (Vision-IMU based identification and ranging) -based generalized obstacle recognition method is proposed. The problem of the low accuracy of obstacle information acquisition in the driving environment where generalized obstacles exist is solved. For generalized obstacles that cannot be detected by the roadside unit, VIDAR obstacle detection is performed on the target generalized obstacles through the vehicle terminal camera, and the detection result information is transmitted to the roadside device terminal through the UDP (User Data Protocol) protocol to achieve obstacle recognition and pseudo-obstacle removal, thereby reducing the error recognition rate of generalized obstacles. In this paper, pseudo-obstacles, obstacles with a certain height less than the maximum passing height of the vehicle, and obstacles with a height greater than the maximum passing height of the vehicle are defined as generalized obstacles. Pseudo-obstacles refer to non-height objects that appear to be “patches” on the imaging interface obtained by visual sensors and obstacles with a height less than the maximum passing height of the vehicle. VIDAR is a vision-IMU-based detection and ranging method. IMU is used to obtain the distance and pose of the camera movement, and through the inverse perspective transformation, it can calculate the height of the object in the image. The VIDAR-based obstacle detection method, the roadside unit-based obstacle detection method, YOLOv5 (You Only Look Once version 5), and the method proposed in this paper were applied to outdoor comparison experiments. The results show that the accuracy of the method is improved by 2.3%, 17.4%, and 1.8%, respectively, compared with the other four methods. Compared with the roadside unit obstacle detection method, the speed of obstacle detection is improved by 1.1%. The experimental results show that the method can expand the detection range of road vehicles based on the vehicle obstacle detection method and can quickly and effectively eliminate false obstacle information on the road.

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Image dataset creation and networks improvement method based on CAD model and edge operator for object detection in the manufacturing industry

Tang

Guo

Han

et al. 2021

Machine Vision and Applications

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Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data

Cited by 4 publications

References 29 publications

Optical flow method for measuring deformation of soil specimen subjected to torsional shearing

Optical flow method for measuring deformation of soil specimen subjected to torsional shearing

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Image dataset creation and networks improvement method based on CAD model and edge operator for object detection in the manufacturing industry

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