Modeling depth for nonparametric foreground segmentation using RGBD devices

Moyà-Alcover, Gabriel; Elgammal, Ahmed; Jaume-i-Capó, Antoni; Varona, Javier

doi:10.1016/j.patrec.2016.09.004

Cited by 31 publications

(28 citation statements)

References 13 publications

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“…40,41 Depth data are usually noisy and have some restrictions for certain surfaces in measurement which typically is referred to as "holes" 31 or "Absent Depth Observations (ADO)" in the literature. 11 These failures come from several physical phenomena such as the production of depth camouflage, depth shadows, absorption by black objects, limitation on distances, lower sensitivity at longer distances and absent observations, etc. Figure 1 illustrates the amount of possible noise in each depth frame, for example the black speaker absorbs the signal and consequently the area is defined as absent of observation (shown by black points) or in some part of the cavity, depth is not available due to the characteristics of the scene.…”

Section: Motion Detectionmentioning

confidence: 99%

Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

Dorudian

Lauria

Swift

2019

International Journal of Micro Air Vehicles

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A novel approach to detect micro air vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving micro air vehicles by combining colour and depth information. The proposed method stores several colour and depth images as models and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background model update presented uses different criteria from existing methods. Additionally, a blind update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different micro air vehicles and publicly available datasets. Results showing some improvements over existing methods are discussed.

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Section: Motion Detectionmentioning

confidence: 99%

Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

Dorudian

Lauria

Swift

2019

International Journal of Micro Air Vehicles

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“…The system stores the first N (N = 20 in our experiments) number of frames to initialise the models (system initialization step). Typically, depth frames are noisy and have some limitations for certain materials, surfaces and black color which known as "holes" [17] or "Absent Depth Observations (ADO) " [3]. In the proposed method these unknown pixel values in the depth frame will be filled by neighboring values before storing in the model.…”

Section: The Proposed Algorithmsmentioning

confidence: 99%

“…One of the most common approaches is to compare the current image with previous images which are known as "reference" in the literature. Generally these references are created from a single image or more compound model which is known as a "scene model" [3]. Traditionally a scene model requires a regular update to adapt to the changes over the time in the real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection Using Adaptive Blind Update and RGB-D Camera

2019

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A novel background subtraction approach using RGB-D camera and an adaptive blind updating policy is introduced. This method in initialization creates a model to store background pixels to compare each pixel of the new frame with the model in the same location to identify background pixels. The background-model update presented in this paper uses regular and blind update which also has a different criteria from existing methods. In particular, blind update frequently changes based on the background changes and the speed of moving object. This will allow the scene model to adapt to the changes in the background, detecting the stationary moving object and reducing the ghost phenomenon. In addition, proposed bootstrapping segmentation and shadow detection are added to the system to improve the accuracy of the algorithm in shadow and depth camouflage scenarios.The proposed method is compared with the original method and other state of the art algorithms. Experimental results show significant improvement in those videos that stationary object appear. In addition, the benchmark results also indicate strong and stable results compared to the other state of the art algorithms.

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“…KDE was used to combine color and depth information for background subtraction after preprocessing. The recent dataset proposed by Gabrilel Moya et al [18] is a new RGB-D dataset for foreground segmentation and generic scene modeling method was designed to analyze the different situations in depth images. Absent Depth Observations (ADO) are divided into two types: caused by scene's physical configuration and resulted from foreground objects edges.…”

Section: A Background Subtraction With Depth Sensorsmentioning

confidence: 99%

Background Subtraction on Depth Videos with Convolutional Neural Networks

Liu

Dong

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Background subtraction is a significant component of computer vision systems. It is widely used in video surveillance, object tracking, anomaly detection, etc. A new data source for background subtraction appeared as the emergence of low-cost depth sensors like Microsoft Kinect, Asus Xtion PRO, etc. In this paper, we propose a background subtraction approach on depth videos, which is based on convolutional neural networks (CNNs), called BGSNet-D (BackGround Subtraction neural Networks for Depth video). The method can be used in color unavailable scenarios like poor lighting situations, and can also be applied to combine with existing RGB background subtraction methods. A preprocessing strategy is designed to reduce the influences incurred by noise from depth sensors. The experimental results on the SBM-RGBD dataset show that the proposed method outperforms existing methods on depth data, and even reaches the performance of the methods that use RGB-D data.

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Modeling depth for nonparametric foreground segmentation using RGBD devices

Cited by 31 publications

References 13 publications

Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

Moving Object Detection Using Adaptive Blind Update and RGB-D Camera

Background Subtraction on Depth Videos with Convolutional Neural Networks

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