Paper
8 June 2023 A method of compressing gait silhouettes based on the graphical edge
Guannan Chen, Shimin Wei
Author Affiliations +
Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127070J (2023) https://doi.org/10.1117/12.2681242
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China
Abstract
As a recognition method that does not require target cooperation, gait recognition has significant value in the criminal investigation field. As the gait recognition network’s input, the gait silhouette occupies a large storage space and low information density, which leads to a high demand for hardware in the gait recognition system. We proposed a compressing gait silhouette method based on the graphical edge, which extracts silhouette edges and combines them into a gait silhouette edge image. Storage space has been reduced by 90.6%, and information density has increased from 4.64% to 49.48%-71.09%. Since the new image is not applicable to the original network, we proposed the improved network GSEInet of GaitSet. The experimental results show that using our compressed images can reduce the network running time by 91.1%, and the demand for graphics card memory by 48.8%, while the recognition rate will not decrease. This will help transplant gait recognition tasks from large servers to small embedded devices and improve flexibility.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guannan Chen and Shimin Wei "A method of compressing gait silhouettes based on the graphical edge", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127070J (8 June 2023); https://doi.org/10.1117/12.2681242
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KEYWORDS
Gait analysis

Image compression

Cameras

Convolution

Feature extraction

Image processing

Interpolation

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