Tracking by segmentation with future motion estimation applied to person-following robots - Jiang, Shenlu; Cui, Runze; Wei, Runze; Fu, Zhiyang; Hong, Zhonghua; Feng, Guofu (Frontiers in Neurorobotics, 2023)
Person-following is a crucial capability for service robots, and the employment of vision technology is a leading trend in building environmental understanding. While most existing methodologies rely on a tracking-by-detection strategy, which necessitates extensive datasets for training and yet remains susceptible to environmental noise, we propose a novel approach: a real-time tracking-by-segmentation with future motion estimation framework. This framework facilitates pixel-level tracking of a target individual and predicts their future motion.Our strategy leverages the single-shot segmentation tracking neural network for precise foreground segmentation to track the target, overcoming the limitations of using a rectangular region of interest (ROI). Here we clarify that, while the ROI provides a broad context, the segmentation within this bounding box offers a detailed and more accurate position of the human subject.To further enhance our approach, a classification-lock pre-trained layer is utilized to form a constraint, curbing feature outliers originating from the person being tracked. A discriminative correlation filter estimates the potential target region in the scene to prevent foreground misrecognition, while a motion estimation neural network anticipates the target's future motion for use in the control module.We validate our proposed methodology using the VOT, LaSot, Youtube-VOS, and Davis tracking datasets, demonstrating its effectiveness. Notably, our framework supports long-term personfollowing tasks in indoor environments, showing promise for practical implementation in service robots.