Version 1
: Received: 11 October 2024 / Approved: 11 October 2024 / Online: 11 October 2024 (13:35:15 CEST)
How to cite:
Salcedo, E. Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures. Preprints2024, 2024100903. https://doi.org/10.20944/preprints202410.0903.v1
Salcedo, E. Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures. Preprints 2024, 2024100903. https://doi.org/10.20944/preprints202410.0903.v1
Salcedo, E. Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures. Preprints2024, 2024100903. https://doi.org/10.20944/preprints202410.0903.v1
APA Style
Salcedo, E. (2024). Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures. Preprints. https://doi.org/10.20944/preprints202410.0903.v1
Chicago/Turabian Style
Salcedo, E. 2024 "Computer Vision-Based Gait Recognition on the Edge: A Survey on Feature Representations, Models, and Architectures" Preprints. https://doi.org/10.20944/preprints202410.0903.v1
Abstract
Computer vision-based gait recognition (CVGR) is a biometric technology that has gained considerable attention in recent years due to its non-invasive, unobtrusive, and difficult-to-conceal nature. Current CVGR systems often transmit collected data to a cloud server for machine-learning-based gait pattern recognition. While effective, this cloud-centric approach can lead to increased system response times. Alternatively, the emerging paradigm of edge computing, which involves moving computational processes to local devices, offers the potential to reduce latency, enable real-time surveillance, and eliminate reliance on internet connectivity. Furthermore, recent advancements in low-cost, compact microcomputers capable of handling complex inference tasks (e.g., Jetson Nano, Xavier NX, AGX Xavier, Raspberry Pi, and Khadas) have created exciting opportunities for deploying CVGR systems at the edge. This paper reports the state of the art in gait data acquisition modalities, feature representations, models, and architectures for CVGR systems amenable for edge computing. Moreover, this paper addresses the general limitations and highlights new avenues for future research in the promising intersection of CVGR and edge computing.
Keywords
Gait recognition; gait analysis; edge computing; computer vision; edge ai
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
