Vehicle-Level Traffic Accident Detection on Vehicle-Mounted Camera Based on Cascade Bi-LSTM

Son, Hyeon-Cheol; Kim, Da-Seul; Kim, Sung-Young

doi:10.14801/jaitc.2020.10.2.167

jaitc

2020

DOI: 10.14801/jaitc.2020.10.2.167

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Vehicle-Level Traffic Accident Detection on Vehicle-Mounted Camera Based on Cascade Bi-LSTM

Hyeon-Cheol Son¹,

Da-Seul Kim²,

Sung-Young Kim³

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2022

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Semantic Detection of Vehicle Violation Video Based on Computer 3D Vision

Dai

2022

Advances in Multimedia

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In order to study the semantic detection accuracy of 3D vehicle accident video, an accident detection method combining 2D image and 3D information was proposed. The 3D semantic bounding box generated by the 3D detection and tracking task of the vehicle is used to extract the motion features of the vehicle, it includes the trajectory of the vehicle and the dimension and orientation of the 3D bounding frame, and the 3D semantic bounding frame is used to establish the evaluation index of the accident detection. The experimental results show that the average loss function of each batch of 1000 images is calculated by the stochastic gradient descent method to update the parameter values. The learning rate was set to 0.001 in the first 30,000 iterations and 0.0001 in the last 10,000 iterations. The MOTA of the CEM algorithm is 78.4%, FP is 1.1%, and FN is 3.5%, and the MOTA of the 3-DCMK algorithm is 88.6%, FP is 0.9%, and FN is 1.9%. The MOTA of this method is 89.3%, FP is 0.9%, and FN is 1.2%. The 3D target semantic detection of vehicle accident video has stability and accuracy.

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Semantic Detection of Vehicle Violation Video Based on Computer 3D Vision

Dai

2022

Advances in Multimedia

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Vehicle-Level Traffic Accident Detection on Vehicle-Mounted Camera Based on Cascade Bi-LSTM

Cited by 1 publication

References 10 publications

Semantic Detection of Vehicle Violation Video Based on Computer 3D Vision

Semantic Detection of Vehicle Violation Video Based on Computer 3D Vision

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