Co-occurrence probability-based pixel pairs background model for robust object detection in dynamic scenes

Liang, Dong; Kaneko, Shun’ichi; Hashimoto, Manabu; Iwata, Kenji; Zhao, Xinyue

doi:10.1016/j.patcog.2014.10.020

Cited by 61 publications

(41 citation statements)

References 22 publications

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“…[21] modeled appearance changes by incrementally learning a tensor subspace representation by adaptively updating the sample mean and an eigenbasis for each unfolding matrix. In our previous research, we pay attention on co-occurrence pixel-pair background models [22,23,24,25]. The models employed an alignment of supporting pixels for the target pixel which held a stable intensity subtraction in training frames without any restriction of locations.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Attention Model for Foreground Detection in Cross-Scene Surveillance Videos

Liang

Pan

Sun

et al. 2019

Sensors

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Foreground detection is an important theme in video surveillance. Conventional background modeling approaches build sophisticated temporal statistical model to detect foreground based on low-level features, while modern semantic/instance segmentation approaches generate high-level foreground annotation, but ignore the temporal relevance among consecutive frames. In this paper, we propose a Spatio-Temporal Attention Model (STAM) for cross-scene foreground detection. To fill the semantic gap between low and high level features, appearance and optical flow features are synthesized by attention modules via the feature learning procedure. Experimental results on CDnet 2014 benchmarks validate it and outperformed many state-of-the-art methods in seven evaluation metrics. With the attention modules and optical flow, its F-measure increased 9% and 6% respectively. The model without any tuning showed its cross-scene generalization on Wallflower and PETS datasets. The processing speed was 10.8 fps with the frame size 256 by 256.

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Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Attention Model for Foreground Detection in Cross-Scene Surveillance Videos

Liang

Pan

Sun

et al. 2019

Sensors

Self Cite

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“…The results are compared with 6 state-of-the-art methods, including MSSTBM [27], GMM-Zivkovic [56], CP3-Online [25], GMM-Stauffer [39], KDE-Elgammal [11] and RMoG [40] by using implementations of the original authors. Foreground detection is compared using Average F measure across all the video sequences within each category.…”

Section: Evaluation Of Deep Context Prediction (Dcp) For Foreground Dmentioning

confidence: 99%

Unsupervised deep context prediction for background estimation and foreground segmentation

Sultana

Mahmood

Javed

et al. 2018

Machine Vision and Applications

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In many high level vision applications such as tracking and surveillance, background estimation is a fundamental step. In the past, background estimation was usually based on low level hand-crafted features such as raw color components, gradients, or local binary patterns. These existing algorithms observe performance degradation in the presence of various challenges such as dynamic backgrounds, photo-metric variations, camera jitter, and shadows. To handle these challenges for the purpose of accurate background estimation, we propose a unified method based on Generative Adversarial Network (GAN) and image inpainting. It is an unsupervised visual feature learning hybrid GAN based on context prediction. It is followed by a semantic inpainting network for texture optimization. We also propose a solution of arbitrary region inpainting by using center region inpainting and Poisson blending. The proposed algorithm is compared with the existing algorithms for background estimation on SBM.net dataset and for foreground segmentation on CDnet 2014 dataset. The proposed algorithm has outperformed the compared methods with significant margin.

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“…In addition to the low-level image features, i.e., grayscale, color intensity and edge magnitudes [1,2,[5][6][7][8], specific feature descriptors can be designed for enhanced performance [3][4]. The background modelling techniques in the literature are loosely categorized into parametric [10][11][12][13] and non-parametric [14][15][16][17][18][19][20] techniques. A detailed classification of background modelling techniques can be found in [9].…”

Section: Related Workmentioning

confidence: 99%

CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction

Mandal

Saxena

Vipparthi

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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Background subtraction in video provides the preliminary information which is essential for many computer vision applications. In this paper, we propose a sequence of approaches named CANDID to handle the change detection problem in challenging video scenarios. The CANDID adaptively initializes the pixel-level distance threshold and update rate. These parameters are updated by computing the change dynamics at a location. Further, the background model is maintained by formulating a deterministic update policy. The performance of the proposed method is evaluated over various challenging scenarios such as dynamic background and extreme weather conditions. The qualitative and quantitative measures of the proposed method outperform the existing state-of-the-art approaches.

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Co-occurrence probability-based pixel pairs background model for robust object detection in dynamic scenes

Cited by 61 publications

References 22 publications

Spatio-Temporal Attention Model for Foreground Detection in Cross-Scene Surveillance Videos

Spatio-Temporal Attention Model for Foreground Detection in Cross-Scene Surveillance Videos

Unsupervised deep context prediction for background estimation and foreground segmentation

CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction

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