Motion and speed estimation play a key role in computer vision and video processing for various application scenarios. Existing algorithms are mainly based on projected and apparent motion models and are currently used in many contexts, such as automotive security and driver assistance, industrial automation and inspection systems, video surveillance, human activity tracking and biomedical solutions, including monitoring of vital signs. In this paper, a general Maximum Likelihood (ML) approach to speed estimation of foreground objects in video streams is proposed. Application examples are presented and the performance of the proposed algorithms is discussed and compared with more conventional solutions.
A maximum likelihood approach to speed estimation of foreground objects in video signals / Mattioli, V.; Alinovi, D.; Raheli, R.. - ELETTRONICO. - 2021-:(2021), pp. 715-719. (Intervento presentato al convegno 28th European Signal Processing Conference, EUSIPCO 2020 tenutosi a nld nel 2020) [10.23919/Eusipco47968.2020.9287813].
A maximum likelihood approach to speed estimation of foreground objects in video signals
Mattioli V.;Alinovi D.;Raheli R.
2021-01-01
Abstract
Motion and speed estimation play a key role in computer vision and video processing for various application scenarios. Existing algorithms are mainly based on projected and apparent motion models and are currently used in many contexts, such as automotive security and driver assistance, industrial automation and inspection systems, video surveillance, human activity tracking and biomedical solutions, including monitoring of vital signs. In this paper, a general Maximum Likelihood (ML) approach to speed estimation of foreground objects in video streams is proposed. Application examples are presented and the performance of the proposed algorithms is discussed and compared with more conventional solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
