Extended Codebook with Multispectral Sequences for Background Subtraction

Liu, Rongrong; Ruichek, Yassine; El-Bagdouri, Mohammed

doi:10.3390/s19030703

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…To evaluate the performance of the proposed approaches for background subtraction, the MVS dataset presented by Benezeth et al [11] is adopted for testing in this section. The proposed approaches are also compared with other methods using the same dataset [5]. Both visual and numerical results are displayed.…”

Section: Resultsmentioning

confidence: 99%

“…The results of the proposed methods are further compared in Table 3 with other methods [5] using the same dataset, in which, the brightness (B), spectral distortion (SD) and spectral information divergence (SID) are adopted to calculate the spectral distance in the matching process. The average F-measures for From average F-measures for the whole dataset, our approaches produce comparable results to the best method that utilizes all the three criteria in [5] (4 th column in Table 3), which proves the effectiveness of our methods. As we can see for the method adopting B and SD, the accuracy for outdoor scenes outperforms in average all the other mechanisms, but it achieves less satisfactory result for the indoor video.…”

Section: Resultsmentioning

confidence: 99%

“…As the name suggests, it is the process to automatically generate a binary mask which classifies the set of pixels into foreground and background, during which, the moving objects are always called the foreground and the static information is called the background. Thus, background subtraction is sometimes also known as foreground detection and foreground-background segmentation [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multispectral Dynamic Codebook and Fusion Strategy for Moving Objects Detection

Liu

Ruichek

Bagdouri

2020

Lecture Notes in Computer Science

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The Codebook model is one of the popular real-time models for background subtraction to detect moving objects. In this paper, we propose two techniques to adapt the original Codebook algorithm to multispectral images: dynamic mechanism and fusion strategy. For each channel, only absolute spectral value is used to calculate the spectral similarity between the current frame pixel and reference average value in the matching process, which can simplify the matching equations. Besides, the deciding boundaries are obtained based on statistical information extracted from the data and always adjusting themselves to the scene changes. Results demonstrate that with the proposed techniques, we can acquire a comparable accuracy with other methods using the same multispectral dataset for background subtraction.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multispectral Dynamic Codebook and Fusion Strategy for Moving Objects Detection

Liu

Ruichek

Bagdouri

2020

Lecture Notes in Computer Science

Self Cite

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“…In terms of the traditional methods, the foreground extraction methods that are designed for stationary cameras commonly consist of three main parts: background model design and initialization, the comparison between the current frame and background model, and background model maintenance. The competitive methods include Gaussian mixture model (GMM) based [ 16 ], sample-based [ 5 ], and codebook based [ 17 ] methods. They are designed to tackle different traditional challenges such as hard shadow, illumination change, dynamic background, and camera jitter, but their applications are limited by the assumption that the camera is stationary.…”

Section: Related Workmentioning

confidence: 99%

Deep Features Homography Transformation Fusion Network—A Universal Foreground Segmentation Algorithm for PTZ Cameras and a Comparative Study

Tao

Zhou

2020

Sensors

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The foreground segmentation method is a crucial first step for many video analysis methods such as action recognition and object tracking. In the past five years, convolutional neural network based foreground segmentation methods have made a great breakthrough. However, most of them pay more attention to stationary cameras and have constrained performance on the pan–tilt–zoom (PTZ) cameras. In this paper, an end-to-end deep features homography transformation and fusion network based foreground segmentation method (HTFnetSeg) is proposed for surveillance videos recorded by PTZ cameras. In the kernel of HTFnetSeg, there is the combination of an unsupervised semantic attention homography estimation network (SAHnet) for frames alignment and a spatial transformed deep features fusion network (STDFFnet) for segmentation. The semantic attention mask in SAHnet reinforces the network to focus on background alignment by reducing the noise that comes from the foreground. STDFFnet is designed to reuse the deep features extracted during the semantic attention mask generation step by aligning the features rather than only the frames, with a spatial transformation technique in order to reduce the algorithm complexity. Additionally, a conservative strategy is proposed for the motion map based post-processing step to further reduce the false positives that are brought by semantic noise. The experiments on both CDnet2014 and Lasiesta show that our method outperforms many state-of-the-art methods, quantitively and qualitatively.

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“…Recent literature on multispectral polarized BS has focused mainly on binary (or foreground-background) segmentation [41] after fusion [42]. Without considering some constraints or assumptions, BS in multiband images seems to be very difficult due to an object appearing in some spectra exhibiting a low correlation [35].…”

Section: Segmentationmentioning

confidence: 99%

Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery

2020

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Multispectral Polarimetric Imagery (MSPI) contains significant information about an object’s distribution, shape, shading, texture and roughness features which can distinguish between foreground and background in a complex scene. Due to spectral signatures being limited to material properties, Background Segmentation (BS) is a difficult task when there are shadows, illumination and clutter in a scene. In this work, we propose a two-fold BS approach: multiband image fusion and polarimetric BS. Firstly, considering that the background in a scene is polarized by nature, the spectral reflectance and correlations and the textural features of MSPI are calculated and analyzed to demonstrate the fusion significance. After that, integrating Principal Component Analysis (PCA) with Fast Fourier Transform (FFT), a hybrid fusion technique is proposed to show the multiband fusion effectiveness. Secondly, utilizing the Stokes vector, polarimetric components are calculated to separate a complex scene’s background from its foreground by constructing four significant foreground masks. An intensity-invariant mask is built by differentiating between the median filtering versions of unpolarized and polarized images. A strongly unpolarized foreground mask is also constructed in two different ways, through analyzing the Angle of Linear Polarization (AoLP) and Degree of Linear Polarization (DoLP). Moreover, a strongly polarized mask and a strong light intensity mask are also calculated based on the azimuth angle and the total light intensity. Finally, all these masks are combined, and a morphological operation is applied to segment the final background area of a scene. The proposed two-fold BS algorithm is evaluated using distinct statistical measurements and compared with well-known fusion methods and BS methods highlighted in this paper. The experimental results demonstrate that the proposed hybrid fusion method significantly improves multiband fusion quality. Furthermore, the proposed polarimetric BS approach also improves the mean accuracy, geometric mean and F1-score to 0.95, 0.93 and 0.97, respectively, for scenes in the MSPI dataset compared with those obtained from the methods in the literature considered in this paper. Future work will investigate mixed polarized and unpolarized BS in the MSPI dataset with specular reflection.

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Extended Codebook with Multispectral Sequences for Background Subtraction

Cited by 8 publications

References 18 publications

Multispectral Dynamic Codebook and Fusion Strategy for Moving Objects Detection

Multispectral Dynamic Codebook and Fusion Strategy for Moving Objects Detection

Deep Features Homography Transformation Fusion Network—A Universal Foreground Segmentation Algorithm for PTZ Cameras and a Comparative Study

Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery

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