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

• URL: http://arxiv.org/abs/2008.09644v1
• Date: Fri, 21 Aug 2020 18:44:35 GMT
• Title: Blending of Learning-based Tracking and Object Detection for Monocular Camera-based Target Following
• Authors: Pranoy Panda, Martin Barczyk
• Abstract summary: 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 emphfamiliar objects.
• Score: 2.578242050187029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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