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논문 기본 정보

자료유형
학술저널
저자정보
Byeong-Uk Jeon (Kyonggi University) Kyungyong Chung (Kyonggi University)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.3
발행연도
2023.6
수록면
261 - 268 (8page)
DOI
10.5573/IEIESPC.2023.12.3.261

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초록· 키워드

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Computer vision technology is used for autonomous driving and road traffic safety. Accordingly, studies on deep learning models that detect and analyze objects through images or videos are ongoing. On the other hand, deep learning algorithms that detect even the action of things require high computing performance. In addition, the computing performance of autonomous driving vehicles for processing such tasks is inferior. These classification processes are not used for recognizing and determining autonomous driving vehicles because it is impossible to process the classification of the action of autonomous driving vehicles in an autonomous driving vehicle on a real-time basis. This paper proposes a Dynamic Framerate SlowFast network for improving autonomous driving performance. Unlike pre-existing studies, the proposed model includes a cropping process through the YOLO model. In addition, it measures the similarity between unit frames through the SSIM and skips the input when the similarity exceeds a certain level. This process made it possible to reduce the number of frames entered into the model. Compared to the existing SlowFast Network, the performance evaluation compared the time required to analyze one image and the AUC of classification results when the number of input frames was reduced through similarity analysis techniques. The similarity analysis technique achieved the highest AUC when the SSIM was applied. The Dynamic Framerate SlowFast network proposed in this study achieved an AUC of 0.7126 and took an FPS of 0.7912 to analyze the entire verification video data. Compared to the pre-existing SlowFast network, which took an FPS of 0.5285 to achieve an AUC of 0.7531, the Dynamic Framerate SlowFast network achieved faster and more accurate results. Therefore, using the proposed technique, it is possible to achieve faster detection results while maintaining the object action detection AUC of the SlowFast network.
#YOLO #SlowFast network #Action recognition #Autonomous driving

목차

Abstract
1. Introduction
2. Related Work
3. Dynamic Framerate SlowFast Network for Improving Autonomous Driving Performance
4. Experiments
5. Conclusion
References

참고문헌 (20)

참고문헌 신청
H. Yoo and K. Chung, “Classification of Multi-Frame Human Motion Using CNN-based Skeleton Extraction,” Intelligent Automation & Soft Computing, Vol. 34, No. 1, pp. 1-13, Apr. 2022. Article (CrossRef Link) google schola H. Yoo and K. Chung, "Deep Learning-based Evolutionary Recommendation Model for Heterogeneous Big Data Integration," KSII Transactions on Internet and Information Systems, 14(9), pp. 3730-3744, Sep. 2020. Article (CrossRef Link) google schola J. Sang, Z. Wu, P. Guo, H. Hu, H. Xiang, Q. Zhang, and B. Cai, "An improved YOLOv2 for vehicle detection," Sensors, Vol. 18, No. 12, pp. 4272, Dec. 2018. Article (CrossRef Link) google schola K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask r-cnn," in Proc. of IEEE/CVF ICCV, pp. 2961-2969, 2017. Article (CrossRef Link) google schola T. H. Wu, T. W. Wang, and Y. Q. Liu, "Real-time vehicle and distance detection based on improved yolo v5 network," in Proc. of 2021 3rd WSAI, pp. 24-28, Jun. 2021. Article (CrossRef Link) google schola

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