A Fuzzy System for Background Modeling in Video Sequences

Calvo-Gallego, Elisa; Brox, Piedad; Sánchez-Solano, Santiago

doi:10.1007/978-3-319-03200-9_19

Cited by 2 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides that, [67] introduced a spatial coherence variant incorporated with the self-organizing neural network to formulate a fuzzy model to enhance the robustness against false detection in the background subtraction algorithm. [68] used both the particle swarm optimization and the kernel least mean square to update the system parameters of a fuzzy model, and [69] employed a tuning process using the Marquardt-Levenberg algorithm within a fuzzy system to fine-tune the membership function. In order to determine the appropriate threshold value for the classification task, [70] proposed a novel fuzzy-cellular method that helps in dynamically learning the optimal threshold value.…”

Section: Hybrid Techniquementioning

confidence: 99%

See 1 more Smart Citation

Fuzzy human motion analysis: A review

Lim

Vats

Chan

2015

Pattern Recognition

View full text Add to dashboard Cite

Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.

show abstract

Section: Hybrid Techniquementioning

confidence: 99%

“…[ 66,67,68,69,70] Integration of the machine learning techniques with the fuzzy approaches allow the system to learn the optimum parameters that leads to better overall system performance and the feasibility to adapt to various situations depending on the task in hand.…”

Section: Fuzzy Integralmentioning

confidence: 99%