Real-Time Multi-Object Tracking and Segmentation with Generated Data using 3D-modelling
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Examensarbete för masterexamen
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Sammanfattning
Multi-Object Tracking and Segmentation (MOTS) is an important branch of computer
vision that has applications in many different areas. In recent developments
these methods have been able to reach favorable speed-accuracy trade-offs, making
them interesting for real-time applications. In this work different deep learning
based MOTS methods have been investigated with the purpose of extending the
DeepTrack framework with real-time MOTS capabilities. Deep learning methods
rely heavily on the data on which they are trained. The collection and annotation of
the data can however be very time-consuming. Therefor, a pipeline is developed and
investigated that automatically produces synthetic data by utilizing 3D-modelling.
The most accurate tracker achieves a MOTSA score of 94 and the tracker with the
best speed-accuracy trade-off achieves a MOTSA score of 88. It is also observed
that satisfactory results can be achieved in most situations with a quite general
data generation pipeline, indicating that the developed pipeline could be used in
different scenarios.
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Ämne/nyckelord
deep learning, neural networks, multi-object tracking and segmentation, synthetic data, PointTrack, SipMask