Real-time Foreground Object Detection Combining the PBAS Background Modelling Algorithm and Feedback from Scene Analysis Module

Kryjak, Tomasz; Komorkiewicz, Mateusz; Gorgon, Marek Boguslaw

doi:10.2478/eletel-2014-0006

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…As part of future work, the proposed algorithms could be improved by adding more advanced fusion of RGB and depth data, as well as detection of static objects. For example, the approach proposed in [8] could be applied. In addition, it is worth to consider preparing a set of sequences registered with various sensors: Kinect (like SBM RGBD), RealSense, a stereo camera and a ToF sensor.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Foreground object segmentation in RGB-D data implemented on GPU

Janus¹,

Kryjak²,

Gorgon³

2020

Preprint

Self Cite

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“…Simultaneously, several improvements to PBAS were proposed such as the hardware implementation [47] or real-time application [48,49].…”

Section: Pbas Methodsmentioning

confidence: 99%

“…In fact, this method updates the background with probability

p = 1 / T false(x_{i} false)

. Simultaneously, several improvements to PBAS were proposed such as the hardware implementation [47] or real‐time application [48, 49].…”

Section: Pbas Methodsmentioning

confidence: 99%

Background modelling using discriminative motion representation

Zhong

et al. 2017

IET Computer Vision

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Robustness is an important factor for background modelling on various scenarios. Current pixel-based adaptive segmentation method cannot effectively tackle diverse objects simultaneously. To address this problem, in this study, a background modelling method using discriminative motion representation is proposed. Instead of simple usage of intensity to construct the background model, the proposed method extracts a new local descriptor which uses a weighted combination of differential excitations for each pixel to enhance the discriminability of pixels. On the basis of this background model, different categories of objects can be quickly identified by a simple but effective classification rule and accurately be represented in background model by a smart selection of updating strategies. Therefore, the authors' background modelling method can generate complete representation for static objects and decrease false detection caused by dynamic background or illumination variations. Extensive experiments have been conducted to demonstrate that the proposed method obtains more advantages of foreground detection than the state-of-the-art methods. In addition, the proposed method provides a computational efficient algorithm for foreground detection tasks.

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“…Barnich, et al [17] used ViBe, which works at a very high speed and creates a background model using neighboring pixels and past pixels of the given pixel. Hofmann et al [18] Kryjak et al [19] used another background subtraction technique called Pixel-Based Adaptive Segmenter (PBAS) that maintains multiple background samples in the background model. In this, the decision threshold and learning parameters are dynamic per-pixel.…”

Section: Background Subtraction Techniquesmentioning

confidence: 99%

Compact Hardware of Running Gaussian Average Algorithm for Moving Object Detection Realized on FPGA and ASIC

Kumar¹,

Nandan²,

Mishra³

2019

RIA

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Real-time detection of moving object is essential to traffic monitoring, video surveillance and many other vital applications. The effectiveness of real-time detection hinges on background subtraction. This paper proposes a hardware-efficient architecture for the running Gaussian average (RGA) technique of background subtraction. The architecture was designed based on the field programmable gate array (FPGA) and application specific integrated circuit (ASIC). The FPGA was realized using Digilent ZedBoard, while the ASIC was implemented using Cadence Genus, Innovus, and Assura tools at 45 nm process technology. A prototype of our architecture was tested with a video containing multiple moving objects, and another containing a single moving object. The results show that the proposed architecture is efficient in terms of area, power and timing. Therefore, this paper provides a background subtraction approach that uses hardware resources effectively without sacrificing the detection accuracy.

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Real-time Foreground Object Detection Combining the PBAS Background Modelling Algorithm and Feedback from Scene Analysis Module

Cited by 13 publications

References 17 publications

Foreground object segmentation in RGB-D data implemented on GPU

Foreground object segmentation in RGB-D data implemented on GPU

Background modelling using discriminative motion representation

Compact Hardware of Running Gaussian Average Algorithm for Moving Object Detection Realized on FPGA and ASIC

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