Deterministic vs. Probabilistic Sensing Models for Geometrical Camera Coverage Modeling
Classical literature in sensor networks classifies the sensor detectability into deterministic or probabilistic sensing models. However, sensing models used in camera coverage modeling lack a proper association with respect to the aforementioned classification. This paper focuses on sensing models u...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2021
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124149032&doi=10.1109%2fICIAS49414.2021.9642623&partnerID=40&md5=782c0150f9197b8a4df2903ee0cc0544 http://eprints.utp.edu.my/29197/ |
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| Summary: | Classical literature in sensor networks classifies the sensor detectability into deterministic or probabilistic sensing models. However, sensing models used in camera coverage modeling lack a proper association with respect to the aforementioned classification. This paper focuses on sensing models used to represent the detection in visual sensor coverage. The paper reviews the sensing models taxonomy used in modeling camera coverage and extrapolates a more relevant sensing model classification to be used with the geometrical camera coverage modeling. Finally, the paper carries out a simulation to highlight the variations of the reviewed sensing models. Thus, a typical camera placement scenario is used to evaluate the implementation of the reviewed sensing models. © 2021 IEEE. |
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