Publication: Recommendations for evaluating the performance of background subtraction algorithms for surveillance systems
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Coadvisor
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Undergraduate course
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Springer
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Abstract
Background subtraction is a prerequisite for a wide range of applications, including video surveillance systems. A significant number of algorithms are often developed and published in different publication mediums in the area, such as workshops, symposiums, conferences, and journals. An important task in presenting a new background subtraction algorithms is to clearly show that its performance outperforms the performance of the state-of-the-art algorithms. In this paper, we present recommendations on how to evaluate the performance of background subtraction algorithms for surveillance systems. We identified, through a systematic mapping, the key steps and components of this evaluation process - procedures, methods, and tools - most used by the authors in each of these steps. Considering this statistical analysis, we perform a theoretical analysis of the most used key components to identify their pros and cons. Then, we define a set of recommendations that aim to standardize and clarify the performance evaluation process of a new background subtraction algorithm.
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Keywords
Background subtraction, Performance assessment, Recommendations, Surveillance systems
Language
English
Citation
Multimedia Tools And Applications. Dordrecht: Springer, v. 80, n. 3, p. 4421-4454, 2021.
