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Title:
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TUNING INTERSECTION-OVER-UNION ALGORITHM TO ENHANCE TRACKING PERFORMANCES |
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Author(s):
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Lorenzo Calisti, Chiara Contoli, Nicholas Kania and Emanuele Lattanzi |
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ISBN:
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978-989-8704-62 |
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Editors:
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Paula Miranda and Pedro Isaías |
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Year:
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2024 |
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Edition:
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Single |
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Keywords:
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Multi-Object Tracking Algorithm, Performance Tuning, Parameter Sensitivity, Monte Carlo Experiments |
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Type:
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Full |
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First Page:
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83 |
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Last Page:
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89 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In computer vision, object tracking remains a pivotal challenge, significantly impacting the performance of various
applications such as autonomous driving, surveillance, and robotics. The Intersection-Over-Union algorithm is a
lightweight and efficient method for online multi-object tracking. This characteristic allows it to be deployed on
low-performance devices, including edge devices and battery-powered devices such as unmanned aerial vehicles.
Unfortunately, the performance of the algorithm depends on several parameters that, if not appropriately sized, can make
it useless. This paper deeply characterizes these dependencies by means of a Monte Carlo exploration of the design space.
As a result of the empirical experiments, an optimal combination of input parameters is proposed to maximally increase
the performance without affecting the lightness of the algorithm. |
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