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

Panda, Pranoy and Barczyk, Martin (2021) Blending of Learning-based Tracking and Object Detection for Monocular Camera-based Target Following. In: 24th International Symposium on Mathematical Theory of Networks and Systems, MTNS 2020, 23 August 2021 through 27 August 2021, Cambridge.

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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. Copyright © 2021 The Authors. This is an open access article under the CC BY-NC-ND license.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: 62m45; 68w27; Tracking, image recognition, neural-network models, data fusion, robot vision. ams subject classifications: 68t45
Subjects: Physics > Mechanical and aerospace
Computer science
Divisions: Department of Computer Science & Engineering
Department of Mechanical & Aerospace Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 08 Aug 2022 05:50
Last Modified: 08 Aug 2022 05:50
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