Foreground object detection from videos containing complex background

By Liyuan Li, Weimin Huang, Irene Yu-Hua Gu, Qi Tian. MULTIMEDIA '03, 2003

483 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/5507060ad65205536d1361b93b45278955792707

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Xiaogang Wang
2. S. Li
3. Dacheng Tao
4. Luc Van Gool
5. Zhen Lei
6. Yi Yang
7. Bernt Schiele
8. Sébastien Marcel

In Safety, Risk, Reliability and Quality

1. Junjie Yan
2. Olivier Barnich
3. S. Li
4. Shengcai Liao
5. Marc Van Droogenbroeck

Citations in the corpus

• Ensemble Video Object Cut in Highly Dynamic Scenes (2013, methodology): “The data used for our performance evaluation consists of existing benchmark datasets and our in-house wildlife monitoring videos: (A) WavingTrees [25]; (B) Fountain [23]; (C) Combination with 9 complex scenes [13, 12]; (D) WaterObject with a jug floating on the rippling water [27]; (E) RainCar with ”
• iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering (2020, background): “are available at: iperceive.amanchadha.com 1. Introduction Today’s computer vision systems are good at telling us the “what” (e.g., classification [20], segmentation [10]) and “where” (e.g., detection [31], localization [52], tracking [63]). Common-sense reasoning [43], which leads to the interesting”
• Evaluation of background subtraction techniques for video surveillance (2011, background): “McFarlane [20] 1995 unimodal (median) color pixel yes DM Stauffer [26] 1999 multimodal (Gaussian) color pixel yes DM Oliver [22] 2000 linear subspace pixel correlation (color) frame no DM McKenna [21] 2000 unimodal (Gaussian) chromaticity, gradient pixel yes DM Li 2 [ 18 ] 2003 non-parametric (discr”
• Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes (2010, methodology): “First column: two frames from the ShoppingMall dataset [4] , with two 10×10 blocks drawn.”
• Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, methodology): “The detected change image was then analyzed to find if the changes correlate to the prediction of Bayesian background model [78] or are they likely to be foreground.”
• Foreground object detection in highly dynamic scenes using saliency (2014, background): “ACMMM03 datasets These datasets contain nine videos of different dynamic scenes.”
• ViBe: A Universal Background Subtraction Algorithm for Video Sequences (2011, methodology): “In terms of PCC scores, only the Bayesian algorithm of [70] based upon histograms competes with ViBe.”
• Competition on counter measures to 2-D facial spoofing attacks (2011, methodology): “We computed two more vitality scores on videos: the first one is based on the average movement caught by a common motion detection technique [16], the second one depends on the number of eye blinks [4].”