An innovative web-based collaborative platform for video annotation

106 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Finely-grained annotated datasets for image-based plant phenotyping (2016, background): “Implementing in the future such tools in web-based environments would permit the crowd-sourcing of annotations (Kavasidis et al., 2014; Goff et al., 2011).”
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, methodology): “[307] designed a spatio-Contextual GMM (SC-GMM) that outperforms 18 background subtraction algorithms such as 1) classical algorithms like the original MOG, the original KDE and the original PCA, and 2) advanced algorithms like DPGMM (VarDMM) [132], PBAS [142], PBAS-PID [356], SuBSENSE [340] and SOB”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, methodology): “• Fish4knowledge Dataset: The Fish4knowledge 35 dataset [138] is an underwater benchmark dataset for target detection against complex background which consists of 14 videos categorized into seven different classes representing complex challenges in background modeling.”
Detection of Marine Animals in a New Underwater Dataset with Varying Visibility (2019, background): “F4K - Complex [24] Reef Stationary Varying RGB 14 videos Bounding box F4K - Species [8] Reef Stationary Varying RGB 27,370 Masks F4K - Trajectories [7] Reef Stationary Varying RGB 93 videos Bounding box J-EDI [17] Deep sea ROV Clear RGB 1,500,000 Image level Croatian Fish Dataset [19] Various Varyin”