Human action recognition in dynamic vision sensors using improved YOLOv3

Hanyu Xiong; Jingjing Liu; Shiwei Ma

doi:10.1117/12.2581347

10 November 2020 Human action recognition in dynamic vision sensors using improved YOLOv3

Hanyu Xiong, Jingjing Liu, Shiwei Ma

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115841V (2020) https://doi.org/10.1117/12.2581347
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

With the development and maturity of object detection techniques, more and more researchers begin to utilize deep learning methods for object detection and classification of the image data obtained by dynamic vision sensors(DVS). Considering that the event stream data obtained by DVS does not have grayscale feature information, we want to convert it into a frame image and utilize the YOLOv3 neural network model for learning to achieve object detection. Since the IoU loss function in the original YOLOv3 network cannot represent the distance between the output predicted box and the grounding truth, this paper improves YOLOv3 by employing the GIoU loss function to achieve more accurate detection. By conducting experiments on a self-collected dataset, we find that the GIoU-improved YOLOv3 network has good performance and can accurately achieve human action recognition and classification.

Citation Download Citation

Hanyu Xiong, Jingjing Liu, and Shiwei Ma "Human action recognition in dynamic vision sensors using improved YOLOv3", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115841V (10 November 2020); https://doi.org/10.1117/12.2581347

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KEYWORDS

Detection and tracking algorithms

Sensors

Data modeling

Data conversion

Image classification

Neural networks

Object recognition

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