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

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 prese...

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Bibliographic Details
Published inIFAC-PapersOnLine Vol. 54; no. 9; pp. 743 - 748
Main Authors Panda, Pranoy, Barczyk, Martin
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.01.2021
Subjects
Data fusion
Image recognition
Neural-network models
Robot vision
Tracking
Data fusion
Image recognition
Neural-network models
Tracking
Robot vision
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Summary: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 familiar objects. Our proposed approach is sufficiently lightweight to track objects at 85-90 FPS while attaining competitive results on challenging benchmarks.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2021.06.172