Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm

Xiao-qin, Zhou; Liu, Xiaofeng; Jiang, Aimin; Yan, Bin; Yang, Chenguang

doi:10.3390/s17051177

Cited by 26 publications

(17 citation statements)

References 24 publications

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“…A similar approach was presented in [20]. The authors fused segmentations results from two ViBE models: for colour and depth.…”

Section: Previous Workmentioning

confidence: 99%

Foreground object segmentation in RGB-D data implemented on GPU

Janus¹,

Kryjak²,

Gorgon³

2020

Preprint

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This paper presents a GPU implementation of two foreground object segmentation algorithms: Gaussian Mixture Model (GMM) and Pixel Based Adaptive Segmenter (PBAS) modified for RGB-D data support. The simultaneous use of colour (RGB) and depth (D) data allows to improve segmentation accuracy, especially in case of colour camouflage, illumination changes and occurrence of shadows. Three GPUs were used to accelerate calculations: embedded NVIDIA Jetson TX2 (Maxwell architecture), mobile NVIDIA GeForce GTX 1050m (Pascal architecture) and efficient NVIDIA RTX 2070 (Turing architecture). Segmentation accuracy comparable to previously published works was obtained. Moreover, the use of a GPU platform allowed to get real-time image processing. In addition, the system has been adapted to work with two RGB-D sensors: RealSense D415 and D435 from Intel.

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“…A similar approach was presented in [20]. The authors fused segmentations results from two ViBE models: for colour and depth.…”

Section: Previous Workmentioning

confidence: 99%

Foreground object segmentation in RGB-D data implemented on GPU

Janus¹,

Kryjak²,

Gorgon³

2020

Preprint

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“…Many popular background subtraction methods need a sequence of frames to initialize the background model [12,30]. However, in some application scenarios, we hope to segment the foreground in few initialization sequences or even from the second frame on.…”

Section: Background Model Initializationmentioning

confidence: 99%

Robust Background Subtraction via the Local Similarity Statistical Descriptor

et al. 2017

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Abstract:Background subtraction based on change detection is the first step in many computer vision systems. Many background subtraction methods have been proposed to detect foreground objects through background modeling. However, most of these methods are pixel-based, which only use pixel-by-pixel comparisons, and a few others are spatial-based, which take the neighborhood of each analyzed pixel into consideration. In this paper, inspired by a illumination-invariant feature based on locality-sensitive histograms proposed for object tracking, we first develop a novel texture descriptor named the Local Similarity Statistical Descriptor (LSSD), which calculates the similarity between the current pixel and its neighbors. The LSSD descriptor shows good performance in illumination variation and dynamic background scenes. Then, we model each background pixel representation with a combination of color features and LSSD features. These features are then embedded in a low-cost and highly efficient background modeling framework. The color and texture features have their own merits and demerits; they can compensate each other, resulting in better performance. Both quantitative and qualitative evaluations carried out on the change detection dataset are provided to demonstrate the effectiveness of our method.

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“…[6][7][8] Moreover, a morphological segmentation algorithm is developed to deal with the merged handwritten numbers, where the thinning procedure is incorporated to analysis both the foreground and background regions of characters. 9,10 As for the recognition-based segmentation, the target character not only is separated from others, but also is tested via some recognition methods. Once the target character is identified, the segmentation procedure is continued for the rest characters.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of touching characters via tree-indexed demarcation using forward and backward searches

Tang

Zhang

et al. 2017

Advances in Mechanical Engineering

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In this article, a tree-indexed demarcation using forward and backward searches is proposed to segment the seriously touching characters, which is used as an important procedure for the character recognition in the real industrial scenarios. Unlike the segmentation directly depending on the histogram analysis, the reliable and unreliable valleys are first identified from a smoothed projection curve in our method, where a set of image pre-procedures are carefully carried out to catch this smoothed curve. With these operations, two segment binary trees are sequently constructed via the corresponding forwarding and backward searches of the valleys. Then, the delimiter candidates are sophisticatedly selected with a number of well-designed rules. The optimal segmented paths are finally obtained on the width variation measure of the characters, and the isolated characters are achieved. Experiments show that with our tree-indexed demarcation using forward and backward searches method, the touching character groups can be effectively segmented to better serve the industrial automatic recognition system.

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Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm

Cited by 26 publications

References 24 publications

Foreground object segmentation in RGB-D data implemented on GPU

Foreground object segmentation in RGB-D data implemented on GPU

Robust Background Subtraction via the Local Similarity Statistical Descriptor

Segmentation of touching characters via tree-indexed demarcation using forward and backward searches

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