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Generic Object Tracking with NVIDIA Jetson Nano Using Siamese Convolutional Neural Networks

Selberg, Alexander LU (2020) In Master's Theses in Mathematical Sciences FMAM05 20201
Mathematics (Faculty of Engineering)
Abstract (Swedish)
In this thesis, a generic object tracker was constructed that was applied to both a commonly used tracking dataset using a regular computer as well as a robot powered by a small NVIDIA computer. The architecture of the tracker consisted of two parallel convolutional neural networks convolving to a single output. The input consisted of two separate cropped images that were fed into the networks separately. The images depicted an object from an image sequence at time t and t + 1, both centered at the object at time t. The purpose of the network is then to compare the two images and output coordinates for the object’s position at time t + 1.The tracker was successful in following several objects from a commonly used visual object tracking... (More)
In this thesis, a generic object tracker was constructed that was applied to both a commonly used tracking dataset using a regular computer as well as a robot powered by a small NVIDIA computer. The architecture of the tracker consisted of two parallel convolutional neural networks convolving to a single output. The input consisted of two separate cropped images that were fed into the networks separately. The images depicted an object from an image sequence at time t and t + 1, both centered at the object at time t. The purpose of the network is then to compare the two images and output coordinates for the object’s position at time t + 1.The tracker was successful in following several objects from a commonly used visual object tracking dataset but performed inconsistently for different scenarios based on its training time. The size of the tracker became a problem when applying it to the robot, requiring significant size reduction. This had a negative effect on the trackers’ performance. The tracker managed to track at up to 60 fps when used on the computer but only around 10 fps for the robot. It’s likely that the tracking performance and speed of the robot can be improved significantly by optimizing the trackers neural network structure aswell as adjusting its training duration. (Less)
Please use this url to cite or link to this publication:
author
Selberg, Alexander LU
supervisor
organization
course
FMAM05 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Convolutional Neural Networks, Deep Learning, Object Tracking, Machine Learning
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3423-2020
ISSN
1404-6342
other publication id
2020:E49
language
English
id
9022512
date added to LUP
2020-09-14 13:52:15
date last changed
2020-09-14 13:52:15
@misc{9022512,
  abstract     = {{In this thesis, a generic object tracker was constructed that was applied to both a commonly used tracking dataset using a regular computer as well as a robot powered by a small NVIDIA computer. The architecture of the tracker consisted of two parallel convolutional neural networks convolving to a single output. The input consisted of two separate cropped images that were fed into the networks separately. The images depicted an object from an image sequence at time t and t + 1, both centered at the object at time t. The purpose of the network is then to compare the two images and output coordinates for the object’s position at time t + 1.The tracker was successful in following several objects from a commonly used visual object tracking dataset but performed inconsistently for different scenarios based on its training time. The size of the tracker became a problem when applying it to the robot, requiring significant size reduction. This had a negative effect on the trackers’ performance. The tracker managed to track at up to 60 fps when used on the computer but only around 10 fps for the robot. It’s likely that the tracking performance and speed of the robot can be improved significantly by optimizing the trackers neural network structure aswell as adjusting its training duration.}},
  author       = {{Selberg, Alexander}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Generic Object Tracking with NVIDIA Jetson Nano Using Siamese Convolutional Neural Networks}},
  year         = {{2020}},
}