Blending of Learning-based Tracking and Object Detection for Monocular Camera-based Target Following
Abstract
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.
Cite
@article{arxiv.2008.09644,
title = {Blending of Learning-based Tracking and Object Detection for Monocular Camera-based Target Following},
author = {Pranoy Panda and Martin Barczyk},
journal= {arXiv preprint arXiv:2008.09644},
year = {2020}
}
Comments
Accepted in 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020): Cambridge, UK (updated conference date: 23-27 August 2021)