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Blending of Learning-based Tracking and Object Detection for Monocular Camera-based Target Following
- Publication Year :
- 2020
-
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.<br />Comment: Accepted in 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020): Cambridge, UK (updated conference date: 23-27 August 2021)
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2008.09644
- Document Type :
- Working Paper