WisenetMD: Motion Detection Using Dynamic Background Region Analysis

Lee, Sang-ha; Lee, Gyu-cheol; Yoo, Ji-Sang; Kwon, Soonchul

doi:10.3390/sym11050621

Cited by 52 publications

(18 citation statements)

References 26 publications

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“…Therefore, the depth information of the remaining parts except the moving person has to be erased. Thus, the depth information of the background and the structure is erased using background subtraction [28].…”

Section: Background Subtractionmentioning

confidence: 99%

Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

Lee

Kim

Cho

et al. 2020

Sensors

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The use of human gesturing to interact with devices such as computers or smartphones has presented several problems. This form of interaction relies on gesture interaction technology such as Leap Motion from Leap Motion, Inc, which enables humans to use hand gestures to interact with a computer. The technology has excellent hand detection performance, and even allows simple games to be played using gestures. Another example is the contactless use of a smartphone to take a photograph by simply folding and opening the palm. Research on interaction with other devices via hand gestures is in progress. Similarly, studies on the creation of a hologram display from objects that actually exist are also underway. We propose a hand gesture recognition system that can control the Tabletop holographic display based on an actual object. The depth image obtained using the latest Time-of-Flight based depth camera Azure Kinect is used to obtain information about the hand and hand joints by using the deep-learning model CrossInfoNet. Using this information, we developed a real time system that defines and recognizes gestures indicating left, right, up, and down basic rotation, and zoom in, zoom out, and continuous rotation to the left and right.

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Section: Background Subtractionmentioning

confidence: 99%

Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

Lee

Kim

Cho

et al. 2020

Sensors

Self Cite

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“…To train the proposed network, this study used a CDnet dataset [20,21]. This dataset has already been used in [9,22]. The CDnet dataset consists of 31 videos with 91,595 image pairs depicting indoor and outdoor scenes with pedestrians, boats, and trucks captured at different times.…”

Section: Implementation Detailsmentioning

confidence: 99%

Detection of Road Surface Changes from Multi-Temporal Unmanned Aerial Vehicle Images Using a Convolutional Siamese Network

Nguyen

Han

2020

Sustainability

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Road quality commonly decreases due to aging and deterioration of road surfaces. As the number of roads that need to be surveyed increases, general maintenance—particularly surveillance—can be quite costly if carried out using traditional methods. Therefore, using unmanned aerial vehicles (UAVs) and deep learning to detect changes via surveys is a promising strategy. This study proposes a method for detecting changes on road surfaces using pairs of UAV images captured at different times. First, a convolutional Siamese network is introduced to extract the features of an image pair and a Euclidean distance function is applied to calculate the distance between two features. Then, a contrastive loss function is used to enlarge the distance between changed feature pairs and reduce the distance between unchanged feature pairs. Finally, the initial change map is improved based on the preliminary differences between the two input images. Our experimental results confirm the effectiveness of this approach.

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“…It is worth noting that, in this method, semantics are used as prior information. In [37], a method is proposed to analyze dynamic background region and reduce the false positives by checking the false positives again. If foreground is detected in the dynamic background region, it is removed by re-checking false positives from the dynamic background samples.…”

Section: Related Workmentioning

confidence: 99%

Compressive Online Video Background–Foreground Separation Using Multiple Prior Information and Optical Flow

Prativadibhayankaram

Luong

et al. 2018

J. Imaging

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In the context of video background-foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method operates on a small set of measurements taken per frame, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-1 minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background-foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods.

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WisenetMD: Motion Detection Using Dynamic Background Region Analysis

Cited by 52 publications

References 26 publications

Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

Detection of Road Surface Changes from Multi-Temporal Unmanned Aerial Vehicle Images Using a Convolutional Siamese Network

Compressive Online Video Background–Foreground Separation Using Multiple Prior Information and Optical Flow

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