Integrating Human Pose Regression with Motion Analysis and Lightweight Edge Solutions for Advanced Pedestrian Detection

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 12

Year of Publication : 2024

Authors : Priyanka Bagul, Shobha Sachin Nikam, Yogita Ajgar, Shilpa Prafull Khedkar

10.14445/23488379/IJEEE-V11I12P108

How to Cite?

Priyanka Bagul, Shobha Sachin Nikam, Yogita Ajgar, Shilpa Prafull Khedkar, "Integrating Human Pose Regression with Motion Analysis and Lightweight Edge Solutions for Advanced Pedestrian Detection," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 12, pp. 94-99, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I12P108

Abstract:

Recent work has focused on combining human pose regression with complementary techniques to improve pedestrian detection. This method enhances pedestrian tracking and recognition by combining pose estimation, motion analysis and scene perception. A notable trend is the development of lightweight models suitable for edge devices that enable real-time detection in many real-world situations. Furthermore, methods such as domain adaptation and transfer learning are being investigated to enhance the ability of posture regression models to generalize to different datasets and environments. By combining motion analysis, visual perception and position regression, this work seeks to improve pedestrian detection by developing effective models for edge devices. The aim is to improve accuracy and real-time performance while guaranteeing compatibility with a wide range of practical applications through the use of posture estimation to record detailed body points and the development of models that work well on devices with limited processing resources.

Keywords:

Human pose regression, Pedestrian detection, Motion Analysis, Lightweight Edge Solutions.

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