Two-stage dual-channel driving distraction behavior recognition algorithm based on key point detection

Wenjun Cao; Shuaichao Chen; Li Yu

doi:10.1117/12.3030001

8 May 2024 Two-stage dual-channel driving distraction behavior recognition algorithm based on key point detection

Wenjun Cao, Shuaichao Chen, Li Yu

Proceedings Volume 13162, Fourth Symposium on Pattern Recognition and Applications (SPRA 2023); 1316204 (2024) https://doi.org/10.1117/12.3030001
Event: Fourth Symposium on Pattern Recognition and Applications (SPRA2023), 2023, Napoli, Italy

Abstract

As an important part of automobile safety system, distracted driving behavior recognition has important research value. By analyzing the limitations and difficulties of the existing distraction driving recognition methods, this paper proposes a two-stage dual-channel recognition network. In the first stage, the Alphapose key point detection network based on SF3D data set pre-training is used to obtain the driver 's key point information, and the key area heat map is generated based on the Gaussian heat map. It is combined with the original image to form the two-channel input of the second stage. The fusion feature is generated by the feature fusion module based on feature concatenation, and it is used as the input of the second stage ResNet-50 backbone recognition network for recognition. Finally, in order to enhance the recognition effect, this paper introduces spatial and channel attention mechanisms to enhance the learning of interest features. And comparison and ablation experiments are designed for the proposed method. Compared with the benchmark network model, the proposed method improves 2.6 points, which verifies the effectiveness of the algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenjun Cao, Shuaichao Chen, and Li Yu "Two-stage dual-channel driving distraction behavior recognition algorithm based on key point detection", Proc. SPIE 13162, Fourth Symposium on Pattern Recognition and Applications (SPRA 2023), 1316204 (8 May 2024); https://doi.org/10.1117/12.3030001

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KEYWORDS

Feature fusion

Detection and tracking algorithms

Matrices

Image fusion

Convolution

Ablation

Data modeling

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