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

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.