Changedetection.net: A new change detection benchmark dataset

By Nil Goyette, Pierre-Marc Jodoin, Fatih Murat Porikli, Janusz Konrad, Prakash Ishwar. IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, 2012

858 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/8afca838919c28f8e8272cd28b9d1cfbf0720760

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Citations in the corpus

Animal Detection From Highly Cluttered Natural Scenes Using Spatiotemporal Object Region Proposals and Patch Verification (2016, neutral): “We also use Pascal VOC 2007 and 2012 detection dataset [40] as auxiliary data to train a generic object model to be used on CDnet 2014 dataset [39] since it contains objects such as cars and boats.”
SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity (2015, methodology): “For their specific descriptions, refer to [3] and [4].”
The Sixth Visual Object Tracking VOT2018 Challenge Results (2018, methodology): “Several initiatives have been established to promote tracking, such as PETS [95], CAVIAR61, i-LIDS 62, ETISEO63, CDC [25], CVBASE 64, FERET [67], LTDT 65, MOTC [44,76] and Videonet 66, and since 2013 short-term single target visual object tracking has been receiving a strong push toward performance ”
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015, methodology): “th 97 to robustness in presence of bootstrapping, large foreground objects (which often arise in RPCA-based methods) and jittery cameras. Experimental results [319] on the ChangeDetection.net dataset [112] conﬁrm that the proposed method can cope with more outliers and with an underlying matrix of h”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, background): “So, video coding needs effective methods for object detection from static and dynamic environments [57][198] Airport Surveillance Airplanes, people [29] Maritime Surveillance Boats, people [31] Store Surveillance People [4][11][153] Insect Surveillance Honeybees [44][271][146] Fish Surveillance Fish”
The Seventh Visual Object Tracking VOT2019 Challenge Results (2019, methodology): “Tracking research has been historically promoted by several initiatives like PETS [105], CAVIAR 2 , i-LIDS 3 , ETISEO 4 , CDC [31], CVBASE 5 , FERET [77], LTDT 6 , MOTC [55, 83] and Videonet 7 .”
Coupled ensemble graph cuts and object verification for animal segmentation from highly cluttered videos (2015, neutral): “We evaluate our method with 1) the Camera Trap dataset of wildlife monitoring videos and 2) the latest Change Detection Workshop 2014 dataset [13].”
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation (2018, methodology): “l networks progressively renews interest in this ﬁeld since 2014 [213] due to the practical advances in deep neural networks which are currently usable due to the availability of large-scale datasets [62][203] for the training, and the progress in computationalhardware ability1. Bylookingatthestory,”
The Visual Object Tracking VOT2014 Challenge Results (2014, neutral): “Indeed, in computer vision fields like segmentation [19,18], optical-flow computation [3], change detection [24], the ubiquitous access to standard datasets and evaluation protocols has substantially contributed to cross-paper comparison [56].”
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, methodology): “ataset did not cover a very large spectrum of challenges and the GTs are also limited to 20 by video. In 2012, a breakthrough was done by the BMC 2012 dataset and especially by the CDnet 2012 dataset [121] that are very realistic large scale datasets with a big amount of videos and corresponding GTs”
CDnet 2014: An Expanded Change Detection Benchmark Dataset (2014, methodology): “Let us mention the 2014 handbook by Bouwmans et al [6] which, to our knowledge, is the most complete manuscript devoted to change detection recently published.”
Street-view change detection with deconvolutional networks (2016, methodology): “Table III compares our method over different change detection metrics [12] for an FPR of 0.10 and 0.01.”
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension (2019, background): “(Ganitkevitch et al., 2013; Goyette et al., 2012; Liu et al., 2015)), the matrices are often doubly sparse, i.”
Ensemble Video Object Cut in Highly Dynamic Scenes (2013, neutral): “Our extensive experimental results and performance comparisons over a diverse set of challenging videos, including the recent Change Detection Challenge Dataset [7], demonstrate that the proposed method outperforms various state- of-the-art algorithms.”