A New Motion Obstacle Detection Based Monocular-Vision Algorithm

Feng, Yaokai; He, Guanghui; Wu, Qizhou

doi:10.1109/iccia.2016.19

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…The background difference method is widely used in dynamic obstacle extraction [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. In this paper, a roadside unit obstacle extraction method based on the background difference method is used.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Ding

Zhang³

et al. 2023

Sensors

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“…To this end, this chapter uses cascaded Adaboosting learning methods to train human detectors. 4 Fast feature selection algorithm and cascade classifier training.…”

Section: Human Detection Based On Monocular Visionmentioning

confidence: 99%

Human Detection and Motion Recovery Based on Monocular Vision

Liu

2021

Rev Bras Med Esporte

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Objective: Provides interactive games and human animation real motion data and technical options. Therefore, how to complete the position, attitude detection, and motion recovery under monocular vision has become an important research direction. Methods: This paper improves the part-based human detection algorithm and uses the AdaBoost multi-instance learning algorithm to train the part detector. Results: The results show that obtaining blood pressure waveform based on monocular vision pulse wave is feasible and has generalization. Conclusions: The results show the feasibility and accuracy of the gait motion detection, motion recovery and analysis system for human lower limbs based on monocular vision. Level of evidence II; Therapeutic studies - investigation of treatment results.

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“…The background modeling main contains mean, median and GAUSSIAN background modeling [7,8,9]. The main process of frames difference is to subtract every current frame with its former frame to extract the foreground from background [10,11]. But the effect always be affected by the modeling of changing background.…”

Section: Related Workmentioning

confidence: 99%

The Obstacle Avoidance for UAV Based on Improved Frames Difference and Optical Flow

Jin¹,

Yan²,

Ye³

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-As to the outdoor real-time change has a bad influence on the obstacle detection and avoidance for UAV with monocular-version, this paper propose a moving obstacle detection algorithm based on three frames subtraction with adaptive iterative threshold and optical flow. Based on improved three frames subtraction, it can quickly separate the moving object from the UAV's forward version and simplify the complexity of the optical flow algorithm. By using optical flow method in the zone where moving obstacles detected, which effectively decreases the influence in brightness constant and calculates the closer obstacle to avoidance. Through the experiment, results show that this method can detect the moving object and calculate the obstacle need to avoidance in good real-time performance and robustness.

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A New Motion Obstacle Detection Based Monocular-Vision Algorithm

Cited by 4 publications

References 5 publications

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Human Detection and Motion Recovery Based on Monocular Vision

The Obstacle Avoidance for UAV Based on Improved Frames Difference and Optical Flow

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