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Title
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Recognition and tracking of objects in a clustered remote scene environment
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Sub-Title |
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Subject |
Object racking; MACH filter; ASIFT; particle filter; recognition
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Sub-Subject |
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Author |
Haris Masood, Amad Zafar, Umair Ali, Muhammad Atti
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Publish Year |
2021 |
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Diss#. |
https://doi.org/10.32604/cmc.2022.019572 |
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Pages |
1699-1719 |
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Text Language |
English |
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Accession |
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Library Section |
Research Article |
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Abstract |
Object recognition and tracking are two of the most dynamic
research sub-areas that belong to the field of Computer Vision. Computer
vision is one of the most active research fields that lies at the intersection of
deep learning and machine vision. This paper presents an efficient ensemble
algorithm for the recognition and tracking of fixed shapemoving objects while
accommodating the shift and scale invariances that the object may encounter.
The first part uses the Maximum Average Correlation Height (MACH) filter
for object recognition and determines the bounding box coordinates. In
case the correlation based MACH filter fails, the algorithms switches to a
much reliable but computationally complex feature based object recognition
technique i.e., affine scale invariant feature transform (ASIFT). ASIFT is
used to accommodate object shift and scale object variations. ASIFT extracts
certain features from the object of interest, providing invariance in up to six
affine parameters, namely tr
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