Blending of Learning-based Tracking and Object Detection for Monocular Camera-based Target Following

Autor:	Panda, Pranoy, Barczyk, Martin
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	Deep learning has recently started being applied to visual tracking of generic objects in video streams. For the purposes of robotics applications, it is very important for a target tracker to recover its track if it is lost due to heavy or prolonged occlusions or motion blur of the target. We present a real-time approach which fuses a generic target tracker and object detection module with a target re-identification module. Our work focuses on improving the performance of Convolutional Recurrent Neural Network-based object trackers in cases where the object of interest belongs to the category of \emph{familiar} objects. Our proposed approach is sufficiently lightweight to track objects at 85-90 FPS while attaining competitive results on challenging benchmarks. Comment: Accepted in 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020): Cambridge, UK (updated conference date: 23-27 August 2021)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.09644 Zobrazit plný text záznamu View this record from Arxiv