Three-Pronged Compensation and Hysteresis Thresholding for Moving Object Detection in Real-Time Video Surveillance

Yeh, Chia‐Hung; Lin, Chih-Yang; Muchtar, Kahlil; Lai, Hsiang-Erh; Sun, Ming–Ting

doi:10.1109/tie.2017.2669881

Cited by 56 publications

(12 citation statements)

References 20 publications

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“…To improve the adaptability of the system to illumination changes [31], modified the update equation [32], explored 3D multi-variable Gaussian distribution, and [33] suggested a method that can automatically calculate the number of proper Gaussian distributions of each pixel instead of setting a constant. A recent method [34] proposed a new framework integrating hysteresis thresholding and motion compensation. They used two background modeling, GMM and a texture modeling to reduce false positive cases.…”

Section: Background Subtractionmentioning

confidence: 99%

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

et al. 2018

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Section: Background Subtractionmentioning

confidence: 99%

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

et al. 2018

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“…fields of industrial informatics. It has been used in the applications of surveillance [1], unmanned ground vehicles, and multimedia processing. In the past decade, visual object detection has attracted a lot of attention of researchers and developers.…”

mentioning

confidence: 99%

Active Object Detection With Multistep Action Prediction Using Deep Q-Network

Han

Liu²,

Sun³

et al. 2019

IEEE Trans. Ind. Inf.

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In recent years, great success has been achieved in visual object detection, which is one of the fundamental tasks in the field of industrial intelligence. Most of existing methods have been proposed to deal with single well-captured still images, while in practical robotic applications, due to nuisances, such as tiny scale, partial view, or occlusion, one still image may not contain enough information for object detection. However, an intelligent robot has the capability to adjust its viewpoint to get better images for detection. Therefore, active object detection becomes a very important perception strategy for intelligent robots. In this paper, by formulating active object detection as a sequential action decision process, a deep reinforcement learning framework is established to resolve it. Furthermore, a novel deep Q-learning network (DQN) with a dueling architecture is proposed, the network has two separate output channels, one predicts action type and the other predicts action range. By combining the two output channels, the action space is explored more efficiently. Several methods are extensively validated and the results show that the proposed one obtains the best results and predicts action in real time.

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“…The technique uses fast in painting for reconstructing an image. Yeh et al [22] have used hysteresis threshold technique to perform identification of mobile objects. Incorporation of the pyramidal feature is seen in the work carried out by Yuan et al [23].…”

Section: Related Techniquesmentioning

confidence: 99%

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Pushpa¹

2017

ijacsa

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Abstract-The advancement of visual sensing has introduced better capturing of the discrete information from a complex, crowded scene for assisting in the analysis. However, after reviewing existing system, we find that majority of the work carried out till date is associated with significant problems in modeling event detection as well as reviewing abnormality of the given scene. Therefore, the proposed system introduces a model that is capable of identifying the degree of abnormality for an event captured on the crowded scene using unsupervised training methodology. The proposed system contributes to developing a novel region-wise repository to extract the contextual information about the discrete-event for a given scene. The study outcome shows highly improved the balance between the computational time and overall accuracy as compared to the majority of the standard research work emphasizing on event detection.

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Three-Pronged Compensation and Hysteresis Thresholding for Moving Object Detection in Real-Time Video Surveillance

Cited by 56 publications

References 20 publications

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

Active Object Detection With Multistep Action Prediction Using Deep Q-Network

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

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