Human detection from low-resolution video images using 3D convolutional neural network

Hiroki Kanazawa; Yuta Nakamoto; Jiaxin Zhou; Takashi Komuro

doi:10.1117/12.2589829

16 July 2021 Human detection from low-resolution video images using 3D convolutional neural network

Hiroki Kanazawa, Yuta Nakamoto, Jiaxin Zhou, Takashi Komuro

Proceedings Volume 11794, Fifteenth International Conference on Quality Control by Artificial Vision; 117941G (2021) https://doi.org/10.1117/12.2589829
Event: Fifteenth International Conference on Quality Control by Artificial Vision, 2021, Tokushima, Japan

Abstract

In this paper, we propose a method for human detection from low-resolution camera images. The proposed method uses video images as input and uses 3D-CNN for classification, which is an extension of 2D-CNN and that can take into account temporal features such as gait motion. In our experiments, we used Caltech Pedestrian Detection Benchmark to make datasets of low-resolution still and video images and compared the performance between 2D-CNN and 3D-CNN. As a result, 3D-CNN with low-resolution video images achieved 91.8 % accuracy rate, 99.0 % precision rate, and 82.8 % recall rate, and showed higher performance than 2D-CNN with low-resolution images, and comparable performance than 2D-CNN with high-resolution images.

Citation Download Citation

Hiroki Kanazawa, Yuta Nakamoto, Jiaxin Zhou, and Takashi Komuro "Human detection from low-resolution video images using 3D convolutional neural network", Proc. SPIE 11794, Fifteenth International Conference on Quality Control by Artificial Vision, 117941G (16 July 2021); https://doi.org/10.1117/12.2589829

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