Recommendations for evaluating the performance of background subtraction algorithms for surveillance systems

• Autor(a) principal: Sanches, Silvio Ricardo Rodrigues
• Data de Publicação: 2020
• Outros Autores: Sementille, Antonio Carlos [UNESP], Aguilar, Ivan Abdo, Freire, Valdinei
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/s11042-020-09838-x; http://hdl.handle.net/11449/210466
• topic: Background subtraction; Performance assessment; Recommendations; Surveillance systems

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.